THE ACS NEARBY GALAXY SURVEY TREASURY

International Nuclear Information System (INIS)

Dalcanton, Julianne J.; Williams, Benjamin F.; Rosema, Keith; Gogarten, Stephanie M.; Christensen, Charlotte; Gilbert, Karoline; Hodge, Paul; Seth, Anil C.; Dolphin, Andrew; Holtzman, Jon; Skillman, Evan D.; Weisz, Daniel; Cole, Andrew; Girardi, Leo; Karachentsev, Igor D.; Olsen, Knut; Freeman, Ken; Gallart, Carme; Harris, Jason; De Jong, Roelof S.

2009-01-01

The ACS Nearby Galaxy Survey Treasury (ANGST) is a systematic survey to establish a legacy of uniform multi-color photometry of resolved stars for a volume-limited sample of nearby galaxies (D 4 in luminosity and star formation rate. The survey data consist of images taken with the Advanced Camera for Surveys (ACS) on the Hubble Space Telescope (HST), supplemented with archival data and new Wide Field Planetary Camera 2 (WFPC2) imaging taken after the failure of ACS. Survey images include wide field tilings covering the full radial extent of each galaxy, and single deep pointings in uncrowded regions of the most massive galaxies in the volume. The new wide field imaging in ANGST reaches median 50% completenesses of m F475W = 28.0 mag, m F606W = 27.3 mag, and m F814W = 27.3 mag, several magnitudes below the tip of the red giant branch (TRGB). The deep fields reach magnitudes sufficient to fully resolve the structure in the red clump. The resulting photometric catalogs are publicly accessible and contain over 34 million photometric measurements of >14 million stars. In this paper we present the details of the sample selection, imaging, data reduction, and the resulting photometric catalogs, along with an analysis of the photometric uncertainties (systematic and random), for both ACS and WFPC2 imaging. We also present uniformly derived relative distances measured from the apparent magnitude of the TRGB.

21 CFR 814.84 - Reports.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Reports. 814.84 Section 814.84 Food and Drugs FOOD... APPROVAL OF MEDICAL DEVICES Postapproval Requirements § 814.84 Reports. (a) The holder of an approved PMA... otherwise, any periodic report shall: (1) Identify changes described in § 814.39(a) and changes required to...

Boosted Jet Tagging with Jet-Images and Deep Neural Networks

International Nuclear Information System (INIS)

Kagan, Michael; Oliveira, Luke de; Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel

2016-01-01

Building on the jet-image based representation of high energy jets, we develop computer vision based techniques for jet tagging through the use of deep neural networks. Jet-images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing. We show how applying such techniques using deep neural networks can improve the performance to identify highly boosted W bosons with respect to state-of-the-art substructure methods. In addition, we explore new ways to extract and visualize the discriminating features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods

VizieR Online Data Catalog: HST/ACS Coma cluster survey. II. (Hammer+, 2010)

NARCIS (Netherlands)

Hammer, D.; Verdoes Kleijn, G.; Hoyos, C.; den Brok, M.; Balcells, M.; Ferguson, H. C.; Goudfrooij, P.; Carter, D.; Guzman, R.; Peletier, R. F.; Smith, R. J.; Graham, A. W.; Trentham, N.; Peng, E.; Puzia, T. H.; Lucey, J. R.; Jogee, S.; Aguerri, A. L.; Batcheldor, D.; Bridges, T. J.; Chiboucas, K.; Davies, J. I.; Del Burgo, C.; Erwin, P.; Hornschemeier, A.; Hudson, M. J.; Huxor, A.; Jenkins, L.; Karick, A.; Khosroshahi, H.; Kourkchi, E.; Komiyama, Y.; Lotz, J.; Marzke, R. O.; Marinova, I.; Matkovic, A.; Merritt, D.; Miller, B. W.; Miller, N. A.; Mobasher, B.; Mouhcine, M.; Okamura, S.; Percival, S.; Phillipps, S.; Poggianti, B. M.; Price, J.; Sharples, R. M.; Tully, R. B.; Valentijn, E.

2010-01-01

This data release contains catalogs for the ACS Images in F475W and F814W bands of 25 fields in the Coma cluster of galaxies. Each field is about 202x202arcsec. Please see the release notes for further details. (25 data files).

Opto-ultrasound imaging in vivo in deep tissue

International Nuclear Information System (INIS)

Si, Ke; YanXu; Zheng, Yao; Zhu, Xinpei; Gong, Wei

2016-01-01

It is of keen importance of deep tissue imaging with high resolution in vivo. Here we present an opto-ultrasound imaging method which utilizes an ultrasound to confine the laser pulse in a very tiny spot as a guide star. The results show that the imaging depth is 2mm with a resolution of 10um. Meanwhile, the excitation power we used is less than 2mW, which indicates that our methods can be applied in vivo without optical toxicity and optical bleaching due to the excitation power. (paper)

VizieR Online Data Catalog: Strong lensing mass modeling of 4 HFF clusters (Kawamata+, 2016)

Science.gov (United States)

Kawamata, R.; Oguri, M.; Ishigaki, M.; Shimasaku, K.; Ouchi, M.

2018-02-01

We use the public HFF data (http://www.stsci.edu/hst/campaigns/frontier-fields/) for our analysis. The HFF targets six massive clusters, Abell 2744 (z=0.308), MACS J0416.1-2403 (z=0.397), MACS J0717.5+3745 (z=0.545), MACS J1149.6+2223 (z=0.541), Abell S1063 (z=0.348), and Abell 370 (z=0.375), which have been chosen according to their lensing strength and also their accessibility from major ground-based telescopes. The cluster core and parallel field region of each cluster are observed deeply with the IR channel of Wide Field Camera 3 (WFC3/IR) and the Advanced Camera for Surveys (ACS). As of 2015 October, HST observations for the first four clusters, Abell 2744, MACS J0416.1-2403, MACS J0717.5+3745, and MACS J1149.6+2223, are completed. In this study, we use the Version 1.0 data products of drizzled images with a pixel scale of 0.03"/pixel provided by the Space Telescope Science Institute (STScI). The images for each cluster consist of F435W (B435), F606W (V606), and F814W (i814) images from ACS, and F105W (Y105), F125W (J125), F140W (JH140), and F160W (H160) images from WFC3/IR. (7 data files).

Measuring metallicities with Hubble space telescope/wide-field camera 3 photometry

Energy Technology Data Exchange (ETDEWEB)

Ross, Teresa L.; Holtzman, Jon A. [Department of Astronomy, New Mexico State University, P.O. Box 30001, MSC 4500, Las Cruces, NM 88003-8001 (United States); Anthony-Twarog, Barbara J.; Twarog, Bruce [Department of Physics and Astronomy, University of Kansas, Lawrence, KS 66045-7582 (United States); Bond, Howard E. [Department of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA 16802 (United States); Saha, Abhijit [National Optical Astronomy Observatory, P.O. Box 26732, Tucson, AZ 85726 (United States); Walker, Alistair, E-mail: rosst@nmsu.edu, E-mail: holtz@nmsu.edu, E-mail: bjat@ku.edu, E-mail: btwarog@ku.edu, E-mail: heb11@psu.edu, E-mail: awalker@ctio.noao.edu [Cerro Tololo Inter-American Observatory (CTIO), National Optical Astronomy Observatory, Casilla 603, La Serena (Chile)

2014-01-01

We quantified and calibrated the metallicity and temperature sensitivities of colors derived from nine Wide-Field Camera 3 filters on board the Hubble Space Telescope using Dartmouth isochrones and Kurucz atmosphere models. The theoretical isochrone colors were tested and calibrated against observations of five well studied galactic clusters, M92, NGC 6752, NGC 104, NGC 5927, and NGC 6791, all of which have spectroscopically determined metallicities spanning –2.30 < [Fe/H] <+0.4. We found empirical corrections to the Dartmouth isochrone grid for each of the following color-magnitude diagrams (CMDs): (F555W-F814W, F814W), (F336W-F555W, F814W), (F390M-F555W, F814W), and (F390W-F555W, F814W). Using empirical corrections, we tested the accuracy and spread of the photometric metallicities assigned from CMDs and color-color diagrams (which are necessary to break the age-metallicity degeneracy). Testing three color-color diagrams [(F336W-F555W),(F390M-F555W),(F390W-F555W), versus (F555W-F814W)], we found the colors (F390M-F555W) and (F390W-F555W) to be the best suited to measure photometric metallicities. The color (F390W-F555W) requires much less integration time, but generally produces wider metallicity distributions and, at very low metallicity, the metallicity distribution function (MDF) from (F390W-F555W) is ∼60% wider than that from (F390M-F555W). Using the calibrated isochrones, we recovered the overall cluster metallicity to within ∼0.1 dex in [Fe/H] when using CMDs (i.e., when the distance, reddening, and ages are approximately known). The measured MDF from color-color diagrams shows that this method measures metallicities of stellar clusters of unknown age and metallicity with an accuracy of ∼0.2-0.5 dex using F336W-F555W, ∼0.15-0.25 dex using F390M-F555W, and ∼0.2-0.4 dex with F390W-F555W, with the larger uncertainty pertaining to the lowest metallicity range.

21 CFR 814.2 - Purpose.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Purpose. 814.2 Section 814.2 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES PREMARKET..., Apr. 1, 2010, § 814.2 was revised, effective Aug. 16, 2010. For the convenience of the user, the...

THE HST/ACS COMA CLUSTER SURVEY. II. DATA DESCRIPTION AND SOURCE CATALOGS

International Nuclear Information System (INIS)

Hammer, Derek; Verdoes Kleijn, Gijs; Den Brok, Mark; Peletier, Reynier F.; Hoyos, Carlos; Balcells, Marc; Aguerri, Alfonso L.; Ferguson, Henry C.; Goudfrooij, Paul; Carter, David; Guzman, Rafael; Smith, Russell J.; Lucey, John R.; Graham, Alister W.; Trentham, Neil; Peng, Eric; Puzia, Thomas H.; Jogee, Shardha; Batcheldor, Dan; Bridges, Terry J.

2010-01-01

The Coma cluster, Abell 1656, was the target of an HST-ACS Treasury program designed for deep imaging in the F475W and F814W passbands. Although our survey was interrupted by the ACS instrument failure in early 2007, the partially completed survey still covers ∼50% of the core high-density region in Coma. Observations were performed for 25 fields that extend over a wide range of cluster-centric radii (∼1.75 Mpc or 1 0 ) with a total coverage area of 274 arcmin 2 . The majority of the fields are located near the core region of Coma (19/25 pointings) with six additional fields in the southwest region of the cluster. In this paper, we present reprocessed images and SEXTRACTOR source catalogs for our survey fields, including a detailed description of the methodology used for object detection and photometry, the subtraction of bright galaxies to measure faint underlying objects, and the use of simulations to assess the photometric accuracy and completeness of our catalogs. We also use simulations to perform aperture corrections for the SEXTRACTOR Kron magnitudes based only on the measured source flux and its half-light radius. We have performed photometry for ∼73,000 unique objects; approximately one-half of our detections are brighter than the 10σ point-source detection limit at F814W = 25.8 mag (AB). The slight majority of objects (60%) are unresolved or only marginally resolved by ACS. We estimate that Coma members are 5%-10% of all source detections, which consist of a large population of unresolved compact sources (primarily globular clusters but also ultra-compact dwarf galaxies) and a wide variety of extended galaxies from a cD galaxy to dwarf low surface brightness galaxies. The red sequence of Coma member galaxies has a color-magnitude relation with a constant slope and dispersion over 9 mag (-21 F814W < -13). The initial data release for the HST-ACS Coma Treasury program was made available to the public in 2008 August. The images and catalogs described

THE PANCHROMATIC HUBBLE ANDROMEDA TREASURY

International Nuclear Information System (INIS)

Dalcanton, Julianne J.; Williams, Benjamin F.; Rosenfield, Philip; Weisz, Daniel R.; Gilbert, Karoline M.; Gogarten, Stephanie M.; Lang, Dustin; Lauer, Tod R.; Dong Hui; Kalirai, Jason S.; Boyer, Martha L.; Gordon, Karl D.; Seth, Anil C.; Dolphin, Andrew; Bell, Eric F.; Bianchi, Luciana C.; Caldwell, Nelson; Dorman, Claire E.; Guhathakurta, Puragra; Girardi, Léo

2012-01-01

The Panchromatic Hubble Andromeda Treasury is an ongoing Hubble Space Telescope Multi-Cycle Treasury program to image ∼1/3 of M31's star-forming disk in six filters, spanning from the ultraviolet (UV) to the near-infrared (NIR). We use the Wide Field Camera 3 (WFC3) and Advanced Camera for Surveys (ACS) to resolve the galaxy into millions of individual stars with projected radii from 0 to 20 kpc. The full survey will cover a contiguous 0.5 deg 2 area in 828 orbits. Imaging is being obtained in the F275W and F336W filters on the WFC3/UVIS camera, F475W and F814W on ACS/WFC, and F110W and F160W on WFC3/IR. The resulting wavelength coverage gives excellent constraints on stellar temperature, bolometric luminosity, and extinction for most spectral types. The data produce photometry with a signal-to-noise ratio of 4 at m F275W = 25.1, m F336W = 24.9, m F475W = 27.9, m F814W = 27.1, m F110W = 25.5, and m F160W = 24.6 for single pointings in the uncrowded outer disk; in the inner disk, however, the optical and NIR data are crowding limited, and the deepest reliable magnitudes are up to 5 mag brighter. Observations are carried out in two orbits per pointing, split between WFC3/UVIS and WFC3/IR cameras in primary mode, with ACS/WFC run in parallel. All pointings are dithered to produce Nyquist-sampled images in F475W, F814W, and F160W. We describe the observing strategy, photometry, astrometry, and data products available for the survey, along with extensive testing of photometric stability, crowding errors, spatially dependent photometric biases, and telescope pointing control. We also report on initial fits to the structure of M31's disk, derived from the density of red giant branch stars, in a way that is independent of assumed mass-to-light ratios and is robust to variations in dust extinction. These fits also show that the 10 kpc ring is not just a region of enhanced recent star formation, but is instead a dynamical structure containing a significant overdensity of

Concurrent fNIRS-fMRI measurement to validate a method for separating deep and shallow fNIRS signals by using multidistance optodes

Science.gov (United States)

Funane, Tsukasa; Sato, Hiroki; Yahata, Noriaki; Takizawa, Ryu; Nishimura, Yukika; Kinoshita, Akihide; Katura, Takusige; Atsumori, Hirokazu; Fukuda, Masato; Kasai, Kiyoto; Koizumi, Hideaki; Kiguchi, Masashi

2015-01-01

Abstract. It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level–dependent signals was analyzed. A three-way analysis of variance (signal depth×Hb kind×task) indicated that the main effect of fNIRS signal depth on the correlation is significant [F(1,1286)=5.34, pdeep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method. PMID:26157983

DIRECT IMAGING CONFIRMATION AND CHARACTERIZATION OF A DUST-ENSHROUDED CANDIDATE EXOPLANET ORBITING FOMALHAUT

International Nuclear Information System (INIS)

Currie, Thayne; Debes, John; Rodigas, Timothy J.; Burrows, Adam; Itoh, Yoichi; Fukagawa, Misato; Kenyon, Scott J.; Kuchner, Marc; Matsumura, Soko

2012-01-01

We present Subaru/IRCS J-band data for Fomalhaut and a (re)reduction of archival 2004-2006 HST/ACS data first presented by Kalas et al. We confirm the existence of a candidate exoplanet, Fomalhaut b, in both the 2004 and 2006 F606W data sets at a high signal-to-noise ratio. Additionally, we confirm the detection at F814W and present a new detection in F435W. Fomalhaut b's space motion may be consistent with it being in an apsidally aligned, non-debris ring-crossing orbit, although new astrometry is required for firmer conclusions. We cannot confirm that Fomalhaut b exhibits 0.7-0.8 mag variability cited as evidence for planet accretion or a semi-transient dust cloud. The new, combined optical spectral energy distribution and IR upper limits confirm that emission identifying Fomalhaut b originates from starlight scattered by small dust, but this dust is most likely associated with a massive body. The Subaru and IRAC/4.5 μm upper limits imply M J , still consistent with the range of Fomalhaut b masses needed to sculpt the disk. Fomalhaut b is very plausibly 'a planet identified from direct imaging' even if current images of it do not, strictly speaking, show thermal emission from a directly imaged planet.

Automatic tissue image segmentation based on image processing and deep learning

Science.gov (United States)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in a deep learning way. We also introduced parallel computing. Such approaches greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.

THE ACS LCID PROJECT. I. SHORT-PERIOD VARIABLES IN THE ISOLATED DWARF SPHEROIDAL GALAXIES CETUS AND TUCANA

NARCIS (Netherlands)

Bernard, Edouard J.; Monelli, Matteo; Gallart, Carme; Drozdovsky, Igor; Stetson, Peter B.; Aparicio, Antonio; Cassisi, Santi; Mayer, Lucio; Cole, Andrew A.; Hidalgo, Sebastian L.; Skillman, Evan D.; Tolstoy, Eline

2009-01-01

We present the first study of the variable star populations in the isolated dwarf spheroidal galaxies (dSphs) Cetus and Tucana. Based on Hubble Space Telescope images obtained with the Advanced Camera for Surveys in the F475W and F814W bands, we identified 180 and 371 variables in Cetus and Tucana,

21 CFR 814.104 - Original applications.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Original applications. 814.104 Section 814.104...) MEDICAL DEVICES PREMARKET APPROVAL OF MEDICAL DEVICES Humanitarian Use Devices § 814.104 Original... applicant. (d) Address for submissions and correspondence. Copies of all original HDEs amendments and...

49 CFR 236.814 - Station, control.

Science.gov (United States)

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Station, control. 236.814 Section 236.814..., MAINTENANCE, AND REPAIR OF SIGNAL AND TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Definitions § 236.814 Station, control. The place where the control machine of a traffic control system is located. ...

POX 186: A Dwarf Galaxy Under Construction?

Science.gov (United States)

Corbin, M. R.; Vacca, W. D.

2000-12-01

We have obtained deep images of the ultracompact ( ~ 3'') blue compact dwarf galaxy POX 186 in the F336W, F555W, and F814W filters of the Planetary Camera of the Hubble Space Telescope. We have additionally obtained a low-resolution near ultraviolet spectrum of the object with STIS and combine this with a ground-based spectrum covering the visible continuum and emission lines. Our images confirm this object to be highly compact, with a maximum projected size of only ~ 240 pc, making it one of the smallest galaxies known. We also confirm that the outer regions of the galaxy consist of an evolved stellar population, ruling out earlier speculations that POX 186 is a protogalaxy. However, the PC images reveal the galaxy to have a highly irregular morphology, with a pronounced tidal arm on its western side. This morphology is strongly suggestive of a recent collision between two smaller components which has in turn triggered the central starburst. The F336W image also shows that the material in this tidal stream is actively star forming. Given the very small ( ~ 100 pc) sizes of the colliding components, POX 186 may be a dwarf galaxy in the early stages of formation, which would be consistent with current ``downsizing'' models of galaxy formation in which the least massive objects are the last to form. This work is supported by NASA and the Space Telescope Science Institute.

46 CFR 176.814 - Steering systems.

Science.gov (United States)

2010-10-01

... 46 Shipping 7 2010-10-01 2010-10-01 false Steering systems. 176.814 Section 176.814 Shipping COAST...) INSPECTION AND CERTIFICATION Material Inspections § 176.814 Steering systems. At each initial and subsequent inspection for certification the owner or managing operator shall be prepared to test the steering systems of...

30 CFR 75.814 - Electrical protection.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Electrical protection. 75.814 Section 75.814... Longwalls § 75.814 Electrical protection. (a) High-voltage circuits must be protected against short circuits... with— (i) Ground-fault protection set to cause deenergization at not more than 40 percent of the...

21 CFR 172.814 - Hydroxylated lecithin.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Hydroxylated lecithin. 172.814 Section 172.814 Food... Multipurpose Additives § 172.814 Hydroxylated lecithin. The food additive hydroxylated lecithin may be safely... obtained by the treatment of lecithin in one of the following ways, under controlled conditions whereby the...

Direct Imaging Confirmation and Characterization of a Dust-Enshrouded Candidate Exoplanet Orbiting Fomalhaut

OpenAIRE

Currie, Thayne; Debes, John; Rodigas, Timothy J.; Burrows, Adam; Itoh, Yoichi; Fukagawa, Misato; Kenyon, Scott; Kuchner, Marc; Matsumura, Soko

2012-01-01

We present Subaru/IRCS J band data for Fomalhaut and a (re)reduction of archival 2004--2006 HST/ACS data first presented by Kalas et al. (2008). We confirm the existence of a candidate exoplanet, Fomalhaut b, in both the 2004 and 2006 F606W data sets at a high signal-to-noise. Additionally, we confirm the detection at F814W and present a new detection in F435W. Fomalhaut b's space motion may be consistent with it being in an apsidally-aligned, non debris ring-crossing orbit, although new astr...

DIRECT IMAGING CONFIRMATION AND CHARACTERIZATION OF A DUST-ENSHROUDED CANDIDATE EXOPLANET ORBITING FOMALHAUT

Energy Technology Data Exchange (ETDEWEB)

Currie, Thayne [Department of Astronomy and Astrophysics, University of Toronto, Toronto, ON (Canada); Debes, John [Space Telescope Science Institute, Baltimore, MD (United States); Rodigas, Timothy J. [Steward Observatory, University of Arizona, Tucson, AZ (United States); Burrows, Adam [Department of Astrophysical Sciences, Princeton University, Princeton, NJ (United States); Itoh, Yoichi [Nishi-Harima Observatory, University of Hyogo, Kobe (Japan); Fukagawa, Misato [Department of Earth and Space Sciences, Osaka University, Osaka (Japan); Kenyon, Scott J. [Smithsonian Astrophysical Observatory, Cambridge, MA (United States); Kuchner, Marc [Stellar and Exoplanets Laboratory, NASA-Goddard Space Flight Center, Greenbelt, MD (United States); Matsumura, Soko, E-mail: currie@astro.utoronto.ca [Department of Astronomy, University of Maryland-College Park, College Park, MD (United States)

2012-12-01

We present Subaru/IRCS J-band data for Fomalhaut and a (re)reduction of archival 2004-2006 HST/ACS data first presented by Kalas et al. We confirm the existence of a candidate exoplanet, Fomalhaut b, in both the 2004 and 2006 F606W data sets at a high signal-to-noise ratio. Additionally, we confirm the detection at F814W and present a new detection in F435W. Fomalhaut b's space motion may be consistent with it being in an apsidally aligned, non-debris ring-crossing orbit, although new astrometry is required for firmer conclusions. We cannot confirm that Fomalhaut b exhibits 0.7-0.8 mag variability cited as evidence for planet accretion or a semi-transient dust cloud. The new, combined optical spectral energy distribution and IR upper limits confirm that emission identifying Fomalhaut b originates from starlight scattered by small dust, but this dust is most likely associated with a massive body. The Subaru and IRAC/4.5 {mu}m upper limits imply M < 2 M{sub J} , still consistent with the range of Fomalhaut b masses needed to sculpt the disk. Fomalhaut b is very plausibly 'a planet identified from direct imaging' even if current images of it do not, strictly speaking, show thermal emission from a directly imaged planet.

UVUDF: Ultraviolet imaging of the Hubble ultra deep field with wide-field camera 3

Energy Technology Data Exchange (ETDEWEB)

Teplitz, Harry I.; Rafelski, Marc; Colbert, James W.; Hanish, Daniel J. [Infrared Processing and Analysis Center, MS 100-22, Caltech, Pasadena, CA 91125 (United States); Kurczynski, Peter; Gawiser, Eric [Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854 (United States); Bond, Nicholas A.; Gardner, Jonathan P.; De Mello, Duilia F. [Laboratory for Observational Cosmology, Astrophysics Science Division, Code 665, Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Grogin, Norman; Koekemoer, Anton M.; Brown, Thomas M.; Coe, Dan; Ferguson, Henry C. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Atek, Hakim [Laboratoire d' Astrophysique, École Polytechnique Fédérale de Lausanne (EPFL), Observatoire, CH-1290 Sauverny (Switzerland); Finkelstein, Steven L. [Department of Astronomy, The University of Texas at Austin, Austin, TX 78712 (United States); Giavalisco, Mauro [Astronomy Department, University of Massachusetts, Amherst, MA 01003 (United States); Gronwall, Caryl [Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802 (United States); Lee, Kyoung-Soo [Department of Physics, Purdue University, 525 Northwestern Avenue, West Lafayette, IN 47907 (United States); Ravindranath, Swara, E-mail: hit@ipac.caltech.edu [Inter-University Centre for Astronomy and Astrophysics, Pune (India); and others

2013-12-01

We present an overview of a 90 orbit Hubble Space Telescope treasury program to obtain near-ultraviolet imaging of the Hubble Ultra Deep Field using the Wide Field Camera 3 UVIS detector with the F225W, F275W, and F336W filters. This survey is designed to: (1) investigate the episode of peak star formation activity in galaxies at 1 < z < 2.5; (2) probe the evolution of massive galaxies by resolving sub-galactic units (clumps); (3) examine the escape fraction of ionizing radiation from galaxies at z ∼ 2-3; (4) greatly improve the reliability of photometric redshift estimates; and (5) measure the star formation rate efficiency of neutral atomic-dominated hydrogen gas at z ∼ 1-3. In this overview paper, we describe the survey details and data reduction challenges, including both the necessity of specialized calibrations and the effects of charge transfer inefficiency. We provide a stark demonstration of the effects of charge transfer inefficiency on resultant data products, which when uncorrected, result in uncertain photometry, elongation of morphology in the readout direction, and loss of faint sources far from the readout. We agree with the STScI recommendation that future UVIS observations that require very sensitive measurements use the instrument's capability to add background light through a 'post-flash'. Preliminary results on number counts of UV-selected galaxies and morphology of galaxies at z ∼ 1 are presented. We find that the number density of UV dropouts at redshifts 1.7, 2.1, and 2.7 is largely consistent with the number predicted by published luminosity functions. We also confirm that the image mosaics have sufficient sensitivity and resolution to support the analysis of the evolution of star-forming clumps, reaching 28-29th magnitude depth at 5σ in a 0.''2 radius aperture depending on filter and observing epoch.

21 CFR 814.20 - Application.

Science.gov (United States)

2010-04-01

... discussion of subject selection and exclusion criteria, study population, study period, safety and... investigator, subject selection and exclusion criteria, study population, study period, safety and... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Application. 814.20 Section 814.20 Food and Drugs...

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

Science.gov (United States)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

49 CFR 178.814 - Hydrostatic pressure test.

Science.gov (United States)

2010-10-01

... 49 Transportation 2 2010-10-01 2010-10-01 false Hydrostatic pressure test. 178.814 Section 178.814... Testing of IBCs § 178.814 Hydrostatic pressure test. (a) General. The hydrostatic pressure test must be... preparation for the hydrostatic pressure test. For metal IBCs, the test must be carried out before the fitting...

21 CFR 814.1 - Scope.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Scope. 814.1 Section 814.1 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES PREMARKET... paragraph (a), effective Aug. 16, 2010. For the convenience of the user, the revised text is set forth as...

Overview of deep learning in medical imaging.

Science.gov (United States)

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

The W-W02 Oxygen Fugacity Buffer at High Pressures and Temperatures: Implications for f02 Buffering and Metal-silicate Partitioning

Science.gov (United States)

Shofner, G. A.; Campbell, A. J.; Danielson, L.; Righter, K.

2013-01-01

Oxygen fugacity (fO2) controls multivalent phase equilibria and partitioning of redox-sensitive elements, and it is important to understand this thermodynamic parameter in experimental and natural systems. The coexistence of a metal and its oxide at equilibrium constitutes an oxygen buffer which can be used to control or calculate fO2 in high pressure experiments. Application of 1-bar buffers to high pressure conditions can lead to inaccuracies in fO2 calculations because of unconstrained pressure dependencies. Extending fO2 buffers to pressures and temperatures corresponding to the Earth's deep interior requires precise determinations of the difference in volume (Delta) V) between the buffer phases. Synchrotron x-ray diffraction data were obtained using diamond anvil cells (DAC) and a multi anvil press (MAP) to measure unit cell volumes of W and WO2 at pressures and temperatures up to 70 GPa and 2300 K. These data were fitted to Birch-Murnaghan 3rd-order thermal equations of state using a thermal pressure approach; parameters for W are KT = 306 GPa, KT' = 4.06, and aKT = 0.00417 GPa K-1. Two structural phase transitions were observed for WO2 at 4 and 32 GPa with structures in P21/c, Pnma and C2/c space groups. Equations of state were fitted for these phases over their respective pressure ranges yielding the parameters KT = 190, 213, 300 GPa, KT' = 4.24, 5.17, 4 (fixed), and aKT = 0.00506, 0.00419, 0.00467 GPa K-1 for the P21/c, Pnma and C2/c phases, respectively. The W-WO2 buffer (WWO) was extended to high pressure by inverting the W and WO2 equations of state to obtain phase volumes at discrete pressures (1-bar to 100 GPa, 1 GPa increments) along isotherms (300 to 3000K, 100 K increments). The slope of the absolute fO2 of the WWO buffer is positive with increasing temperature up to approximately 70 GPa and is negative above this pressure. The slope is positive along isotherms from 1000 to 3000K with increasing pressure up to at least 100 GPa. The WWO buffer is at

Clinical significance of MRI/18F-FDG PET fusion imaging of the spinal cord in patients with cervical compressive myelopathy

International Nuclear Information System (INIS)

Uchida, Kenzo; Nakajima, Hideaki; Watanabe, Shuji; Yoshida, Ai; Baba, Hisatoshi; Okazawa, Hidehiko; Kimura, Hirohiko; Kudo, Takashi

2012-01-01

18 F-FDG PET is used to investigate the metabolic activity of neural tissue. MRI is used to visualize morphological changes, but the relationship between intramedullary signal changes and clinical outcome remains controversial. The present study was designed to evaluate the use of 3-D MRI/ 18 F-FDG PET fusion imaging for defining intramedullary signal changes on MRI scans and local glucose metabolic rate measured on 18 F-FDG PET scans in relation to clinical outcome and prognosis. We studied 24 patients undergoing decompressive surgery for cervical compressive myelopathy. All patients underwent 3-D MRI and 18 F-FDG PET before surgery. Quantitative analysis of intramedullary signal changes on MRI scans included calculation of the signal intensity ratio (SIR) as the ratio between the increased lesional signal intensity and the signal intensity at the level of the C7/T1 disc. Using an Advantage workstation, the same slices of cervical 3-D MRI and 18 F-FDG PET images were fused. On the fused images, the maximal count of the lesion was adopted as the standardized uptake value (SUV max ). In a similar manner to SIR, the SUV ratio (SUVR) was also calculated. Neurological assessment was conducted using the Japanese Orthopedic Association (JOA) scoring system for cervical myelopathy. The SIR on T1-weighted (T1-W) images, but not SIR on T2-W images, was significantly correlated with preoperative JOA score and postoperative neurological improvement. Lesion SUV max was significantly correlated with SIR on T1-W images, but not with SIR on T2-W images, and also with postoperative neurological outcome. The SUVR correlated better than SIR on T1-W images and lesion SUV max with neurological improvement. Longer symptom duration was correlated negatively with SIR on T1-W images, positively with SIR on T2-W images, and negatively with SUV max . Our results suggest that low-intensity signal on T1-W images, but not on T2-W images, is correlated with a poor postoperative neurological

Here Be Dragons: Characterization of ACS/WFC Scattered Light Anomalies

Science.gov (United States)

Porterfield, B.; Coe, D.; Gonzaga, S.; Anderson, J.; Grogin, N.

2016-11-01

We present a study characterizing scattered light anomalies that occur near the edges of Advanced Camera for Surveys (ACS) Wide Field Channel (WFC) images. We inspected all 8,573 full-frame ACS/WFC raw images with exposure times longer than 350 seconds obtained in the F606W and F814W filters from 2002 to October 2013. We visually identified two particular scattered light artifacts known as "dragon's breath" and edge glow. Using the 2MASS point source catalog and Hubble Guide Star Catalog (GSC II), we identified the stars that caused these artifacts. The stars are all located in narrow bands ( 3" across) just outside the ACS/WFC field of view (2" - 16" away). We provide a map of these risky areas around the ACS/WFC detectors - users should avoid positioning bright stars in these regions when designing ACS/WFC imaging observations. We also provide interactive webpages which display all the image artifacts we identified, allowing users to see examples of the severity of artifacts they might expect for a given stellar magnitude at a given position relative to the ACS/WFC field of view. On average, 10th (18th) magnitude stars produce artifacts about 1,000 (100) pixels long. But the severity of these artifacts can vary strongly with small positional shifts (∼ 1"). The results are similar for both filters (F606W and F814W) when expressed in total fluence, or flux multiplied by exposure time.

21 CFR 814.39 - PMA supplements.

Science.gov (United States)

2010-04-01

..., modifications to manufacturing procedures or methods of manufacture that affect the safety and effectiveness of... 21 Food and Drugs 8 2010-04-01 2010-04-01 false PMA supplements. 814.39 Section 814.39 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES...

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

Science.gov (United States)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

Ultra Deep Wave Equation Imaging and Illumination

Energy Technology Data Exchange (ETDEWEB)

Alexander M. Popovici; Sergey Fomel; Paul Sava; Sean Crawley; Yining Li; Cristian Lupascu

2006-09-30

In this project we developed and tested a novel technology, designed to enhance seismic resolution and imaging of ultra-deep complex geologic structures by using state-of-the-art wave-equation depth migration and wave-equation velocity model building technology for deeper data penetration and recovery, steeper dip and ultra-deep structure imaging, accurate velocity estimation for imaging and pore pressure prediction and accurate illumination and amplitude processing for extending the AVO prediction window. Ultra-deep wave-equation imaging provides greater resolution and accuracy under complex geologic structures where energy multipathing occurs, than what can be accomplished today with standard imaging technology. The objective of the research effort was to examine the feasibility of imaging ultra-deep structures onshore and offshore, by using (1) wave-equation migration, (2) angle-gathers velocity model building, and (3) wave-equation illumination and amplitude compensation. The effort consisted of answering critical technical questions that determine the feasibility of the proposed methodology, testing the theory on synthetic data, and finally applying the technology for imaging ultra-deep real data. Some of the questions answered by this research addressed: (1) the handling of true amplitudes in the downward continuation and imaging algorithm and the preservation of the amplitude with offset or amplitude with angle information required for AVO studies, (2) the effect of several imaging conditions on amplitudes, (3) non-elastic attenuation and approaches for recovering the amplitude and frequency, (4) the effect of aperture and illumination on imaging steep dips and on discriminating the velocities in the ultra-deep structures. All these effects were incorporated in the final imaging step of a real data set acquired specifically to address ultra-deep imaging issues, with large offsets (12,500 m) and long recording time (20 s).

Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

Science.gov (United States)

Liu, Fang; Jang, Hyungseok; Kijowski, Richard; Bradshaw, Tyler; McMillan, Alan B

2018-02-01

Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared

Production of W + W - pairs via γ * γ * → W + W - subprocess with photon transverse momenta

Science.gov (United States)

Łuszczak, Marta; Schäfer, Wolfgang; Szczurek, Antoni

2018-05-01

We discuss production of W + W - pairs in proton-proton collisions induced by two-photon fusion including, for a first time, transverse momenta of incoming photons. The unintegrated inelastic fluxes (related to proton dissociation) of photons are calculated based on modern parametrizations of deep inelastic structure functions in a broad range of their arguments ( x and Q 2). In our approach we can get separate contributions of different W helicities states. Several one- and two-dimensional differential distributions are shown and discussed. The present results are compared to the results of previous calculations within collinear factorization approach. Similar results are found except of some observables such as e.g. transverse momentum of the pair of W + and W -. We find large contributions to the cross section from the region of large photon virtualities. We show decomposition of the total cross section as well as invariant mass distribution into the polarisation states of both W bosons. The role of the longitudinal F L structure function is quantified. Its inclusion leads to a 4-5% decrease of the cross section, almost independent of M WW .

Impact of deep learning on the normalization of reconstruction kernel effects in imaging biomarker quantification: a pilot study in CT emphysema

Science.gov (United States)

Jin, Hyeongmin; Heo, Changyong; Kim, Jong Hyo

2018-02-01

Differing reconstruction kernels are known to strongly affect the variability of imaging biomarkers and thus remain as a barrier in translating the computer aided quantification techniques into clinical practice. This study presents a deep learning application to CT kernel conversion which converts a CT image of sharp kernel to that of standard kernel and evaluates its impact on variability reduction of a pulmonary imaging biomarker, the emphysema index (EI). Forty cases of low-dose chest CT exams obtained with 120kVp, 40mAs, 1mm thickness, of 2 reconstruction kernels (B30f, B50f) were selected from the low dose lung cancer screening database of our institution. A Fully convolutional network was implemented with Keras deep learning library. The model consisted of symmetric layers to capture the context and fine structure characteristics of CT images from the standard and sharp reconstruction kernels. Pairs of the full-resolution CT data set were fed to input and output nodes to train the convolutional network to learn the appropriate filter kernels for converting the CT images of sharp kernel to standard kernel with a criterion of measuring the mean squared error between the input and target images. EIs (RA950 and Perc15) were measured with a software package (ImagePrism Pulmo, Seoul, South Korea) and compared for the data sets of B50f, B30f, and the converted B50f. The effect of kernel conversion was evaluated with the mean and standard deviation of pair-wise differences in EI. The population mean of RA950 was 27.65 +/- 7.28% for B50f data set, 10.82 +/- 6.71% for the B30f data set, and 8.87 +/- 6.20% for the converted B50f data set. The mean of pair-wise absolute differences in RA950 between B30f and B50f is reduced from 16.83% to 1.95% using kernel conversion. Our study demonstrates the feasibility of applying the deep learning technique for CT kernel conversion and reducing the kernel-induced variability of EI quantification. The deep learning model has a

Clinical significance of MRI/{sup 18}F-FDG PET fusion imaging of the spinal cord in patients with cervical compressive myelopathy

Energy Technology Data Exchange (ETDEWEB)

Uchida, Kenzo; Nakajima, Hideaki; Watanabe, Shuji; Yoshida, Ai; Baba, Hisatoshi [University of Fukui, Department of Orthopaedics and Rehabilitation Medicine, Faculty of Medical Sciences, Eiheiji, Fukui (Japan); Okazawa, Hidehiko [University of Fukui, Department of Biomedical Imaging Research Center, Eiheiji, Fukui (Japan); Kimura, Hirohiko [University of Fukui, Departments of Radiology, Faculty of Medical Sciences, Eiheiji, Fukui (Japan); Kudo, Takashi [Nagasaki University, Department of Radioisotope Medicine, Atomic Bomb Disease and Hibakusha Medicine Unit, Atomic Bomb Disease Institute, Nagasaki (Japan)

2012-10-15

{sup 18}F-FDG PET is used to investigate the metabolic activity of neural tissue. MRI is used to visualize morphological changes, but the relationship between intramedullary signal changes and clinical outcome remains controversial. The present study was designed to evaluate the use of 3-D MRI/{sup 18}F-FDG PET fusion imaging for defining intramedullary signal changes on MRI scans and local glucose metabolic rate measured on {sup 18}F-FDG PET scans in relation to clinical outcome and prognosis. We studied 24 patients undergoing decompressive surgery for cervical compressive myelopathy. All patients underwent 3-D MRI and {sup 18}F-FDG PET before surgery. Quantitative analysis of intramedullary signal changes on MRI scans included calculation of the signal intensity ratio (SIR) as the ratio between the increased lesional signal intensity and the signal intensity at the level of the C7/T1 disc. Using an Advantage workstation, the same slices of cervical 3-D MRI and {sup 18}F-FDG PET images were fused. On the fused images, the maximal count of the lesion was adopted as the standardized uptake value (SUV{sub max}). In a similar manner to SIR, the SUV ratio (SUVR) was also calculated. Neurological assessment was conducted using the Japanese Orthopedic Association (JOA) scoring system for cervical myelopathy. The SIR on T1-weighted (T1-W) images, but not SIR on T2-W images, was significantly correlated with preoperative JOA score and postoperative neurological improvement. Lesion SUV{sub max} was significantly correlated with SIR on T1-W images, but not with SIR on T2-W images, and also with postoperative neurological outcome. The SUVR correlated better than SIR on T1-W images and lesion SUV{sub max} with neurological improvement. Longer symptom duration was correlated negatively with SIR on T1-W images, positively with SIR on T2-W images, and negatively with SUV{sub max}. Our results suggest that low-intensity signal on T1-W images, but not on T2-W images, is correlated

The HST/ACS Coma Cluster Survey. II. Data Description and Source Catalogs

Science.gov (United States)

Hammer, Derek; Kleijn, Gijs Verdoes; Hoyos, Carlos; Den Brok, Mark; Balcells, Marc; Ferguson, Henry C.; Goudfrooij, Paul; Carter, David; Guzman, Rafael; Peletier, Reynier F.;

On the determination of the He abundance distribution in globular clusters from the width of the main sequence

Science.gov (United States)

Cassisi, Santi; Salaris, Maurizio; Pietrinferni, Adriano; Hyder, David

2017-01-01

One crucial piece of information to study the origin of multiple stellar populations in globular clusters is the range of initial helium abundances ΔY amongst the sub-populations hosted by each cluster. These estimates are commonly obtained by measuring the width in colour of the unevolved main sequence in an optical colour-magnitude diagram (CMD). The measured colour spread is then compared with predictions from theoretical stellar isochrones with varying initial He abundances to determine ΔY. The availability of UV/optical magnitudes, thanks to the Hubble Space Telescope UV Legacy Survey of Galactic GCs project, will allow the homogeneous determination of ΔY for a large Galactic globular cluster sample. From a theoretical point of view, accurate UV CMDs can efficiently disentangle the various sub-populations, and main sequence colour differences in the ACS F606W - (F606W - F814W) diagram allow an estimate of ΔY. We demonstrate that from a theoretical perspective, the (F606W - F814W) colour is an extremely reliable He-abundance indicator. The derivative dY/d(F606W - F814W), computed at a fixed luminosity along the unevolved main sequence, is largely insensitive to the physical assumptions made in stellar model computations, being more sensitive to the choice of the bolometric correction scale, and is only slightly dependent on the adopted set of stellar models. From a theoretical point of view, the (F606W - F814W) colour width of the cluster main sequence is therefore a robust diagnostic of the ΔY range.

Neuronal pathology in deep grey matter structures: a multimodal imaging analysis combining PET and MRI

Energy Technology Data Exchange (ETDEWEB)

Bosque-Freeman, L.; Leroy, C.; Galanaud, D.; Sureau, F.; Assouad, R.; Tourbah, A.; Papeix, C.; Comtat, C.; Trebossen, R.; Lubetzki, C.; Delforge, J.; Bottlaender, M.; Stankoff, B. [Serv. Hosp. Frederic Joliot, Orsay (France)

2009-07-01

Objective: To assess neuronal damage in deep gray matter structures by positron emission tomography (PET) using [{sup 11}C]-flumazenil (FMZ), a specific central benzodiazepine receptor antagonist, and [{sup 18}F]-fluorodeoxyglucose (FDG), which reflects neuronal metabolism. To compare results obtained by PET and those with multimodal magnetic resonance imaging (MRI). Background: It is now accepted that neuronal injury plays a crucial role in the occurrence and progression of neurological disability in multiple sclerosis (MS). To date, available MRI techniques do not specifically assess neuronal damage, but early abnormalities, such as iron deposition or atrophy, have been described in deep gray matter structures. Whether those MRI modifications correspond to neuronal damage remains to be further investigated. Materials and methods: Nine healthy volunteers were compared to 10 progressive and 9 relapsing remitting (RR) MS patients. Each subject performed two PET examinations with [{sup 11}C]-FMZ and [{sup 18}F]-FDG, on a high resolution research tomograph dedicated to brain imaging (Siemens Medical Solution, spatial resolution of 2.5 mm). Deep gray matter regions were manually segmented on T1-weighted MR images with the mutual information algorithm (www.brainvisa.info), and co-registered with PET images. A multimodal MRI including T1 pre and post gadolinium, T2-proton density sequences, magnetization transfer, diffusion tensor, and protonic spectroscopy was also performed for each subject. Results: On PET with [{sup 11}C]-FMZ, there was a pronounced decrease in receptor density for RR patients in all deep gray matter structures investigated, whereas the density was unchanged or even increased in the same regions for progressive patients. Whether the different patterns between RR and progressive patients reflect distinct pathogenic mechanisms is currently investigated by comparing PET and multimodal MRI results. Conclusion: Combination of PET and multimodal MR imaging

Deep learning for staging liver fibrosis on CT: a pilot study.

Science.gov (United States)

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Abe, Osamu; Kiryu, Shigeru

2018-05-14

To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images. This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (F DLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance. The F DLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p deep learning model based on CT images, with moderate performance. • Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance. • Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging. • Further improvement are necessary before utilisation in clinical settings.

Computational ghost imaging using deep learning

Science.gov (United States)

Shimobaba, Tomoyoshi; Endo, Yutaka; Nishitsuji, Takashi; Takahashi, Takayuki; Nagahama, Yuki; Hasegawa, Satoki; Sano, Marie; Hirayama, Ryuji; Kakue, Takashi; Shiraki, Atsushi; Ito, Tomoyoshi

2018-04-01

Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three-dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.

Deep Learning in Medical Image Analysis.

Science.gov (United States)

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

Do cosmological data rule out f (R ) with w ≠-1 ?

Science.gov (United States)

Battye, Richard A.; Bolliet, Boris; Pace, Francesco

2018-05-01

We review the equation of state (EoS) approach to dark sector perturbations and apply it to f (R ) gravity models of dark energy. We show that the EoS approach is numerically stable and use it to set observational constraints on designer models. Within the EoS approach we build an analytical understanding of the dynamics of cosmological perturbations for the designer class of f (R ) gravity models, characterized by the parameter B0 and the background equation of state of dark energy w . When we use the Planck cosmic microwave background temperature anisotropy, polarization, and lensing data as well as the baryonic acoustic oscillation data from SDSS and WiggleZ, we find B0<0.006 (95% C.L.) for the designer models with w =-1 . Furthermore, we find B0<0.0045 and |w +1 |<0.002 (95% C.L.) for the designer models with w ≠-1 . Previous analyses found similar results for designer and Hu-Sawicki f (R ) gravity models using the effective field theory approach [Raveri et al., Phys. Rev. D 90, 043513 (2014), 10.1103/PhysRevD.90.043513; Hu et al., Mon. Not. R. Astron. Soc. 459, 3880 (2016), 10.1093/mnras/stw775]; therefore this hints for the fact that generic f (R ) models with w ≠-1 can be tightly constrained by current cosmological data, complementary to solar system tests [Brax et al., Phys. Rev. D 78, 104021 (2008), 10.1103/PhysRevD.78.104021; Faulkner et al., Phys. Rev. D 76, 063505 (2007), 10.1103/PhysRevD.76.063505]. When compared to a w CDM fluid with the same sound speed, we find that the equation of state for f (R ) models is better constrained to be close to -1 by about an order of magnitude, due to the strong dependence of the perturbations on w .

VizieR Online Data Catalog: The Carnegie-Chicago Hubble Program. II. IC 1613 (Hatt+, 2017)

Science.gov (United States)

Hatt, D.; Beaton, R. L.; Freedman, W. L.; Madore, B. F.; Jang, I.-S.; Hoyt, T. J.; Lee, M. G.; Monson, A. J.; Rich, J. A.; Scowcroft, V.; Seibert, M.

2018-04-01

Observations of IC 1613 were obtained on 2015 June 12 using the Inamori-Magellan Areal Camera and Spectrograph (IMACS) on the 6.5m Magellan-Baade telescope at Las Campanas Observatory. We obtain a 15.46'x15.46' field of view with resolution of 0.2"/pixel and observed in the BVI filters. See section 2.1.1. We have made use of archival imaging of IC 1613 taken from the Local Cosmology from Isolated Dwarfs program (PID:GO10505, PI: Gallart; Gallart 2005, LCID). A single field was imaged over 24 orbits between 2006 August 28 and 30 approximately 5' west of the center of IC 1613 using the HST ACS/WFC instrument, which provides a 202"x202" field of view with 0.05"/pixel resolution. Each orbit was divided between two ~1200s exposures in the F475W and F814W passbands, resulting in 48 epochs per filter. See section 2.1.2. We obtained near-infrared imaging over 24 orbits between 2014 December 17 and 18 using the HST WFC3/IR instrument (PID:GO13691, PI: Freedman; Freedman W. 2014 HST Proposal). The orbits were divided between two overlapping 136"x123" WFC3/IR pointings with a native resolution of 0.135"/pixel. See section 2.1.3. In parallel with the observations described in the previous section were 24 orbits with the HST ACS/WFC instrument (PID:GO13691, PI: Freedman; Freedman 2014 W. HST Proposal). Each exposure in F606W and F814W spanned ~500s. See section 2.1.4. (2 data files).

Hello World Deep Learning in Medical Imaging.

Science.gov (United States)

Lakhani, Paras; Gray, Daniel L; Pett, Carl R; Nagy, Paul; Shih, George

2018-05-03

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.

PSF support pilot program

Science.gov (United States)

Anderson, Jay

2013-10-01

The goal of this program is to observe the center of Omega Cen {which has a nice flat distribution of reasonably-spaced-out stars} in order to construct a PSF model for ACS's three workhorse filters: F435W, F606W, and F814W. These also happen to be the three ACS filters that will be used in the Frontier-Field program. PI-Anderson will use the data to consturct an 9x10 array of fiducial PSFs that describe the static variation of the PSF across the frame for each filter. He will also provide some simple routines that the public can use to insert PSFs into images.The observations will dither the center of the cluster around in a circle with a radius of about 30" such that any single star never falls in the ACS gap more than once. This has the additional benefit that we can use this large dither to validate or improve the distortion solution at the same time we are solving for the PSF. We will get four exposures through each of the ACS filters. The exposure times for the three ACS filters {F435W, F606W, and F814W} were chosen to maximize the number of bright unsaturated stars while simultaneously minimizing the number of saturated stars present. To do this, we made sure that the SGB {which is where the LF rises precipitously} is just below the saturation level. We used archival images from GO-9444 and GO-10775 to ensure that 339s for F435W, 80s in F606W, and 90s in F814W is perfect for this.In addition to the ACS exposures, we also take parallels with WFC3/IR. These exposures will sample a field that is 6' off center. The core radius is 2.5', so this outer field should have a density that is 5x lower than at the center, meaning the typical star is maybe 2.5x farther away. This should compensate for the larger WFC3/IR pixels and will allow us to construct PSFs that are appropriate. We take a total of 32 WFC3/IR exposures, each with an exposure time of 103s, and divide these 32 exposures among the four FF WFC3/IR exposures: F105W, F125W, F140W, and F160W. We will use

Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images.

Science.gov (United States)

Banzato, T; Cherubini, G B; Atzori, M; Zotti, A

2018-05-01

An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Deep learning for SAR image formation

Science.gov (United States)

Mason, Eric; Yonel, Bariscan; Yazici, Birsen

2017-04-01

The recent success of deep learning has lead to growing interest in applying these methods to signal processing problems. This paper explores the applications of deep learning to synthetic aperture radar (SAR) image formation. We review deep learning from a perspective relevant to SAR image formation. Our objective is to address SAR image formation in the presence of uncertainties in the SAR forward model. We present a recurrent auto-encoder network architecture based on the iterative shrinkage thresholding algorithm (ISTA) that incorporates SAR modeling. We then present an off-line training method using stochastic gradient descent and discuss the challenges and key steps of learning. Lastly, we show experimentally that our method can be used to form focused images in the presence of phase uncertainties. We demonstrate that the resulting algorithm has faster convergence and decreased reconstruction error than that of ISTA.

Deep learning for image classification

Science.gov (United States)

McCoppin, Ryan; Rizki, Mateen

2014-06-01

This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

GLOBULAR CLUSTERS INDICATE THAT ULTRA-DIFFUSE GALAXIES ARE DWARFS

Energy Technology Data Exchange (ETDEWEB)

Beasley, Michael A.; Trujillo, Ignacio, E-mail: beasley@iac.es [Instituto de Astrofisica de Canarias, Calle Via Láctea, La Laguna, Tenerife (Spain)

2016-10-10

We present an analysis of archival HST /ACS imaging in the F475W ( g {sub 475}), F606W ( V {sub 606}), and F814W ( I {sub 814}) bands of the globular cluster (GC) system of a large (3.4 kpc effective radius) ultra-diffuse galaxy (DF17) believed to be located in the Coma Cluster of galaxies. We detect 11 GCs down to the 5 σ completeness limit of the imaging ( I {sub 814} = 27 mag). Correcting for background and our detection limits yields a total population of GCs in this galaxy of 27 ± 5 and a V -band specific frequency S {sub N} = 28 ± 5. Based on comparisons to the GC systems of local galaxies, we show that both the absolute number and the colors of the GC system of DF17 are consistent with the GC system of a dark-matter-dominated dwarf galaxy with virial mass ∼9.0 × 10{sup 10} M {sub ⊙} and a dark-to-stellar mass ratio M {sub vir}/ M {sub star} ∼ 1000. Based on the stellar mass growth of the Milky Way, we show that DF17 cannot be understood as a failed Milky-Way-like system, but is more similar to quenched Large-Magellanic-Cloud-like systems. We find that the mean color of the GC population, g {sub 475}– I {sub 814} = 0.91 ± 0.05 mag, coincides with the peak of the color distribution of intracluster GCs and is also similar to those of the blue GCs in the outer regions of massive galaxies. We suggest that both the intracluster GC population in Coma and the blue peak in the GC populations of massive galaxies may be fed—at least in part—by the disrupted equivalents of systems such as DF17.

Assessing microscope image focus quality with deep learning.

Science.gov (United States)

Yang, Samuel J; Berndl, Marc; Michael Ando, D; Barch, Mariya; Narayanaswamy, Arunachalam; Christiansen, Eric; Hoyer, Stephan; Roat, Chris; Hung, Jane; Rueden, Curtis T; Shankar, Asim; Finkbeiner, Steven; Nelson, Philip

2018-03-15

Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of

The HST/ACS Coma Cluster Survey - VII. Structure and assembly of massive galaxies in the centre of the Coma cluster

NARCIS (Netherlands)

Weinzirl, Tim; Jogee, Shardha; Neistein, Eyal; Khochfar, Sadegh; Kormendy, John; Marinova, Irina; Hoyos, Carlos; Balcells, Marc; den Brok, Mark; Hammer, Derek; Peletier, Reynier F.; Kleijn, Gijs Verdoes; Carter, David; Goudfrooij, Paul; Lucey, John R.; Mobasher, Bahram; Trentham, Neil; Erwin, Peter; Puzia, Thomas

2014-01-01

We constrain the assembly history of galaxies in the projected central 0.5 Mpc of the Coma cluster by performing structural decomposition on 69 massive (M⋆ ≥ 109 M⊙) galaxies using high-resolution F814W images from the Hubble Space Telescope (HST) Treasury Survey of Coma. Each galaxy is modelled

Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

Directory of Open Access Journals (Sweden)

Soheila Gheisari

2018-01-01

Full Text Available Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

21 CFR 814.116 - Procedures for review of an HDE.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Procedures for review of an HDE. 814.116 Section 814.116 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES...), effective Aug. 16, 2010. For the convenience of the user, the added text is set forth as follows: § 814.116...

THE IMPORTANCE OF NEBULAR CONTINUUM AND LINE EMISSION IN OBSERVATIONS OF YOUNG MASSIVE STAR CLUSTERS

International Nuclear Information System (INIS)

Reines, Amy E.; Nidever, David L.; Whelan, David G.; Johnson, Kelsey E.

2010-01-01

In this spectroscopic study of infant massive star clusters, we find that continuum emission from ionized gas rivals the stellar luminosity at optical wavelengths. In addition, we find that nebular line emission is significant in many commonly used broadband Hubble Space Telescope (HST) filters including the F814W I-band, the F555W V-band, and the F435W B-band. Two young massive clusters (YMCs) in the nearby starburst galaxy NGC 4449 were targeted for follow-up spectroscopic observations after Reines et al. discovered an F814W I-band excess in their photometric study of radio-detected clusters in the galaxy. The spectra were obtained with the Dual Imaging Spectrograph (DIS) on the 3.5 m Apache Point Observatory (APO) telescope and have a spectral range of ∼3800-9800 A. We supplement these data with HST and Sloan Digital Sky Survey photometry of the clusters. By comparing our data to the Starburst99 and GALEV evolutionary synthesis models, we find that nebular continuum emission competes with the stellar light in our observations and that the relative contribution from the nebular continuum is largest in the U- and I-bands, where the Balmer (3646 A) and Paschen jumps (8207 A) are located. The spectra also exhibit strong line emission including the [S III] λλ9069, 9532 lines in the HST F814W I-band. We find that the combination of nebular continuum and line emission can account for the F814W I-band excess previously found by Reines et al. In an effort to provide a benchmark for estimating the impact of ionized gas emission on photometric observations of young massive stellar populations, we compute the relative contributions of the stellar continuum, nebular continuum, and emission lines to the total observed flux of a 3 Myr old cluster through various HST filter/instrument combinations, including filters in the Wide Field Camera 3. We urge caution when comparing observations of YMCs to evolutionary synthesis models since nebular continuum and line emission can

Image compression using the W-transform

Energy Technology Data Exchange (ETDEWEB)

Reynolds, W.D. Jr. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

1995-12-31

The authors present the W-transform for a multiresolution signal decomposition. One of the differences between the wavelet transform and W-transform is that the W-transform leads to a nonorthogonal signal decomposition. Another difference between the two is the manner in which the W-transform handles the endpoints (boundaries) of the signal. This approach does not restrict the length of the signal to be a power of two. Furthermore, it does not call for the extension of the signal thus, the W-transform is a convenient tool for image compression. They present the basic theory behind the W-transform and include experimental simulations to demonstrate its capabilities.

MRP8/14 induces autophagy to eliminate intracellular Mycobacterium bovis BCG.

Science.gov (United States)

Wang, Jinli; Huang, Chunyu; Wu, Minhao; Zhong, Qiu; Yang, Kun; Li, Miao; Zhan, Xiaoxia; Wen, Jinsheng; Zhou, Lin; Huang, Xi

2015-04-01

To explore the role of myeloid-related protein 8/14 in mycobacterial infection. The mRNA and protein expression levels of MRP8 or MRP14 were measured by real-time PCR and flow cytometry, respectively. Role of MRP8/14 was tested by overexpression or RNA interference assays. Flow cytometry and colony forming unit were used to test the phagocytosis and the survival of intracellular Mycobacterium bovis BCG (BCG), respectively. Autophagy mediated by MRP8/14 was detected by Western blot and immunofluorescence. The colocalization of BCG phagosomes with autophagosomes or lysosomes was by detected by confocal microscopy. ROS production was detected by flow cytometry. MRP8/14 expressions were up-regulated in human monocytic THP1 cells and primary macrophages after mycobacterial challenge. Silencing of MRP8/14 suppressed bacterial killing, but had no influence on the phagocytosis of BCG. Importantly, silencing MRP8/14 decreased autophagy and BCG phagosome maturation in THP1-derived macrophages, thereby increasing the BCG survival. Additionally, we demonstrated that MRP8/14 promoted autophagy in a ROS-dependent manner. The present study revealed a novel role of MRP8/14 in the autophagy-mediated elimination of intracellular BCG by promoting ROS generation, which may provide a promising therapeutic target for tuberculosis and other intracellular bacterial infectious diseases. Copyright © 2014 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

21 CFR 814.80 - General.

Science.gov (United States)

2010-04-01

... APPROVAL OF MEDICAL DEVICES Postapproval Requirements § 814.80 General. A device may not be manufactured, packaged, stored, labeled, distributed, or advertised in a manner that is inconsistent with any conditions...

The translocator protein ligand [{sup 18}F]DPA-714 images glioma and activated microglia in vivo

Energy Technology Data Exchange (ETDEWEB)

Winkeler, Alexandra; Boisgard, Raphael; Awde, Ali R.; Dubois, Albertine; Theze, Benoit; Zheng, Jinzi [Universite Paris Sud, Inserm, U1023, Laboratoire d' Imagerie Moleculaire Experimentale, Orsay (France); CEA, I2BM, SHFJ, Orsay (France); Ciobanu, Luisa [CEA, DSV, I2BM, NeuroSpin, LRMN, Gif sur Yvette (France); Dolle, Frederic [CEA, I2BM, SHFJ, Orsay (France); Viel, Thomas; Jacobs, Andreas H. [Westfaelische Wilhelm-Universitaet Muenster (WWU), European Institute for Molecular Imaging (EIMI), Muenster (Germany); Tavitian, Bertrand [Universite Paris Sud, Inserm, U1023, Laboratoire d' Imagerie Moleculaire Experimentale, Orsay (France)

2012-05-15

In recent years there has been an increase in the development of radioligands targeting the 18-kDa translocator protein (TSPO). TSPO expression is well documented in activated microglia and serves as a biomarker for imaging neuroinflammation. In addition, TSPO has also been reported to be overexpressed in a number of cancer cell lines and human tumours including glioma. Here we investigated the use of [{sup 18}F]DPA-714, a new TSPO positron emission tomography (PET) radioligand to image glioma in vivo. We studied the uptake of [{sup 18}F]DPA-714 in three different rat strains implanted with 9L rat glioma cells: Fischer (F), Wistar (W) and Sprague Dawley (SD) rats. Dynamic [{sup 18}F]DPA-714 PET imaging, kinetic modelling of PET data and in vivo displacement studies using unlabelled DPA-714 and PK11195 were performed. Validation of TSPO expression in 9L glioma cell lines and intracranial 9L gliomas were investigated using Western blotting and immunohistochemistry of brain tissue sections. All rats showed significant [{sup 18}F]DPA-714 PET accumulation at the site of 9L tumour implantation compared to the contralateral brain hemisphere with a difference in uptake among the three strains (F > W > SD). The radiotracer showed high specificity for TSPO as demonstrated by the significant reduction of [{sup 18}F]DPA-714 binding in the tumour after administration of unlabelled DPA-714 or PK11195. TSPO expression was confirmed by Western blotting in 9L cells in vitro and by immunohistochemistry ex vivo. The TSPO radioligand [{sup 18}F]DPA-714 can be used for PET imaging of intracranial 9L glioma in different rat strains. This preclinical study demonstrates the feasibility of employing [{sup 18}F]DPA-714 as an alternative radiotracer to image human glioma. (orig.)

Cross-modal priming facilitates production of low imageability word strings in a case of deep-phonological dysphasia

Directory of Open Access Journals (Sweden)

Laura Mary Mccarthy

2014-04-01

Full Text Available Introduction. Characteristics of repetition in deep-phonological dysphasia include an inability to repeat nonwords, semantic errors in single word repetition (deep dysphasia and in multiple word repetition (phonological dysphasia and better repetition of highly imageable words (Wilshire & Fisher, 2004; Ablinger et al., 2008. Additionally, visual processing of words is often more accurate than auditory processing of words (Howard & Franklin, 1988. We report a case study of LT who incurred a LCVA on 10/3/2009. She initially presented with deep dysphasia and near normal word reading. When enrolled in this study, approximately 24 months post-onset, she presented with phonological dysphasia. We investigated the hypothesis that (1 reproduction of a word string would be more accurate when preceded by a visual presentation of the word string compared to two auditory presentations of the word string, and (2 that this facilitative boost would be observed only for strings of low image words, consistent with the imageability effect in repetition. Method. Three-word strings were created in four conditions which varied the frequency (F and imageability (I of words within a string: HiF-HiI, LoF-HiI, HiF-LoI, LoF-LoI. All strings were balanced for total syllable length and were unrelated semantically and phonologically. The dependent variable was as accuracy of repetition of each word within a string. We created six modality prime conditions each with 24 strings drawn equally from the four frequency-imageability types, randomized within modality condition: Auditory Once (AudOnce – string presented auditorily one time; Auditory Twice (AudAud – string presented auditorily two consecutive times; Visual Once (VisOnce – string presented visually one time; Visual Twice (VisVis – string presented visually two consecutive times; Auditory then Visual (AudVis – string presented once auditorily, then a second time visually; Visual then Auditory (VisAud �

Waarnemingen aan Equisetum arvense L. var. serotinum F.W. Meyer

NARCIS (Netherlands)

Hoek, van L.

1978-01-01

Observations during six successive years in a cultivated clone of Equisetum arvense L. var. serotinum F. W. Meyer made it possible to study various types of fertile, sterile, and homophyadic stems developing in this clone. As E. arvense var. varium Milde, E. arvense f. sanguineum Luerss., and E.

21 CFR 814.37 - PMA amendments and resubmitted PMA's.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false PMA amendments and resubmitted PMA's. 814.37 Section 814.37 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES... heading and paragraph (b), effective Aug. 16, 2010. For the convenience of the user, the revised text is...

SG1120-1202: Mass-quenching as Tracked by UV Emission in the Group Environment at z=0.37

Science.gov (United States)

Monroe, Jonathan T.; Tran, Kim-Vy H.; Gonzalez, Anthony H.

2017-02-01

We use the Hubble Space Telescope to obtain WFC3/F390W imaging of the supergroup SG1120-1202 at z=0.37, mapping the UV emission of 138 spectroscopically confirmed members. We measure total (F390W-F814W) colors and visually classify the UV morphology of individual galaxies as “clumpy” or “smooth.” Approximately 30% of the members have pockets of UV emission (clumpy) and we identify for the first time in the group environment galaxies with UV morphologies similar to the “jellyfish” galaxies observed in massive clusters. We stack the clumpy UV members and measure a shallow internal color gradient, which indicates that unobscured star formation is occurring throughout these galaxies. We also stack the four galaxy groups and measure a strong trend of decreasing UV emission with decreasing projected group distance ({R}{proj}). We find that the strong correlation between decreasing UV emission and increasing stellar mass can fully account for the observed trend in (F390W-F814W)-{R}{proj}, I.e., mass-quenching is the dominant mechanism for extinguishing UV emission in group galaxies. Our extensive multi-wavelength analysis of SG1120-1202 indicates that stellar mass is the primary predictor of UV emission, but that the increasing fraction of massive (red/smooth) galaxies at {R}{proj} ≲ 2 R 200 and existence of jellyfish candidates is due to the group environment.

UVIS Flat Field Uniformity

Science.gov (United States)

Quijano, Jessica Kim

2009-07-01

The stability and uniformity of the low-frequency flat fields {L-flat} of the UVIS detector will be assessed by using multiple-pointing observations of the globular clusters 47 Tucanae {NGC104} and Omega Centauri {NGC5139}, thus imaging moderately dense stellar fields. By placing the same star over different portions of the detector and measuring relative changes in its brightness, it will be possible to determine local variations in the response of the UVIS detector. Based on previous experience with STIS and ACS, it is deemed that a total of 9 different pointings will suffice to provide adequate characterization of the flat field stability in any given band. For each filter to be tested, the baseline consists of 9 pointings in a 3X3 box pattern with dither steps of about 25% of the FOV, or 40.5", in either the x or y direction {useful also for CTE measurements, if needed in the future}. During SMOV, the complement of filters to be tested is limited to the following 6 filters: F225W, F275W, F336W, for Omega Cen, and F438W, F606W, and F814W for 47 Tuc. Three long exposures for each target are arranged such that the initial dither position is observed with the appropriate filters for that target within one orbit at a single pointing, so that filter-to-filter differences in the observed star positions can be checked. In addition to the 9 baseline exposures, two sets of short exposures will be taken:a} one short exposure will be taken of OmegaCen with each of the visible filters {F438W, F606W and F814W} in order to check the geometric distortion solution to be obtained with the data from proposal 11444;b} for each target, a single short exposure will be taken with each filter to facilitate the study of the PSF as a function of position on the detector by providing unsaturated images of sparsely-spaced bright stars.This proposal corresponds to Activity Description ID WF39. It should execute only after the following proposal has executed:WF21 - 11434

Image Captioning with Deep Bidirectional LSTMs

OpenAIRE

Wang, Cheng; Yang, Haojin; Bartz, Christian; Meinel, Christoph

2016-01-01

This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are propose...

19 CFR 10.814 - Direct costs of processing operations.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 1 2010-04-01 2010-04-01 false Direct costs of processing operations. 10.814... Free Trade Agreement Rules of Origin § 10.814 Direct costs of processing operations. (a) Items included. For purposes of § 10.810(b) of this subpart, the words “direct costs of processing operations”, with...

Coherent backscatter radar imaging in Brazil: large-scale waves in the bottomside F-region at the onset of equatorial spread F

Directory of Open Access Journals (Sweden)

F. S. Rodrigues

2008-10-01

Full Text Available The 30 MHz coherent backscatter radar located at the equatorial observatory in São Luís, Brazil (2.59° S, 44.21° W, −2.35° dip lat has been upgraded to perform coherent backscatter radar imaging. The wide field-of-view of this radar makes it well suited for radar imaging studies of ionospheric irregularities. Radar imaging observations were made in support to the spread F Experiment (SpreadFEx campaign. This paper describes the system and imaging technique and presents results from a bottom-type layer that preceded fully-developed radar plumes on 25 October 2005. The radar imaging technique was able to resolve decakilometric structures within the bottom-type layer. These structures indicate the presence of large-scale waves (~35 km in the bottomside F-region with phases that are alternately stable and unstable to wind-driven gradient drift instabilities. The observations suggest that these waves can also cause the initial perturbation necessary to initiate the Generalized Rayleigh-Taylor instability leading to spread F. The electrodynamic conditions and the scale length of the bottom-type layer structures suggest that the waves were generated by the collisional shear instability. These results indicate that monitoring bottom-type layers may provide helpful diagnostics for spread F forecasting.

Measurement of sin2θw and ϱ in deep inelastic neutrino-nucleon scattering

Science.gov (United States)

Reutens, P. G.; Merritt, F. S.; Macfarlane, D. B.; Messner, R. L.; Novikoff, D. B.; Purohit, M. V.; Blair, R. E.; Sciulli, F. J.; Shaevitz, M. H.; Fisk, H. E.; Fukushima, Y.; Jin, B. N.; Kondo, T.; Rapidis, P. A.; Yovanovitch, D. D.; Bodek, A.; Coleman, R. N.; Marsh, W. L.; Fackler, O. D.; Jenkins, K. A.

1985-03-01

We describe a high statistics measurement from deep inelastic neutrino-nucleon scattering of the electroweak parameters ϱ and sin2θw, performed in the Fermilab narrow-band neutrino beam. Our measurement uses a radius-dependent cut in y = EH/Ev which reduces the systematic error in sin2θw, and incorporates electromagnetic and electroweak radiative corrections. In a renormalization scheme where sin2θw ≡ 1-m2W/m2Z, a value of sin2θw = 0.242+/-0.011+/-0.005 is obtained fixing ϱ = 1. If both sin2θw and ϱ are allowed to vary in a fit to our data, we measure ϱ = 0.991 +/- 0.025 +/- 0.009. Present address: IBM Thomas J. Watson Research Center, PO Box 218, Yorktown Heights, NY 10598, USA.

NiftyNet: a deep-learning platform for medical imaging.

Science.gov (United States)

Gibson, Eli; Li, Wenqi; Sudre, Carole; Fidon, Lucas; Shakir, Dzhoshkun I; Wang, Guotai; Eaton-Rosen, Zach; Gray, Robert; Doel, Tom; Hu, Yipeng; Whyntie, Tom; Nachev, Parashkev; Modat, Marc; Barratt, Dean C; Ourselin, Sébastien; Cardoso, M Jorge; Vercauteren, Tom

2018-05-01

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new

Evaluation of a deep learning architecture for MR imaging prediction of ATRX in glioma patients

Science.gov (United States)

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J.

2018-02-01

Predicting mutation/loss of alpha-thalassemia/mental retardation syndrome X-linked (ATRX) gene utilizing MR imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare a deep neural network approach based on a residual deep neural network (ResNet) architecture and one based on a classical machine learning approach and evaluate their ability in predicting ATRX mutation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture, pre trained on ImageNet data was the best performing model, achieving an accuracy of 0.91 for the test set (classification of a slice as no tumor, ATRX mutated, or mutated) in terms of f1 score in a test set of 35 cases. The SVM classifier achieved 0.63 for differentiating the Flair signal abnormality regions from the test patients based on their mutation status. We report a method that alleviates the need for extensive preprocessing and acts as a proof of concept that deep neural network architectures can be used to predict molecular biomarkers from routine medical images.

A Survey on Deep Learning in Medical Image Analysis

NARCIS (Netherlands)

Litjens, G.J.; Kooi, T.; Ehteshami Bejnordi, B.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; Laak, J.A.W.M. van der; Ginneken, B. van; Sanchez, C.I.

2017-01-01

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared

Fast MR Imaging of the Paediatric Abdomen with CAIPIRINHA-Accelerated T1w 3D FLASH and with High-Resolution T2w HASTE: A Study on Image Quality

Directory of Open Access Journals (Sweden)

Mengxia Li

2015-01-01

Full Text Available The aim of this study was to explore the applicability of fast MR techniques to routine paediatric abdominopelvic MRI at 1.5 Tesla. “Controlled Aliasing in Parallel Imaging Results in Higher Acceleration-” (CAIPIRINHA- accelerated contrast-enhanced-T1w 3D FLASH imaging was compared to standard T1w 2D FLASH imaging with breath-holding in 40 paediatric patients and to respiratory-triggered T1w TSE imaging in 10 sedated young children. In 20 nonsedated patients, we compared T2w TIRM to fat-saturated T2w HASTE imaging. Two observers performed an independent and blinded assessment of overall image quality. Acquisition time was reduced by the factor of 15 with CAIPIRINHA-accelerated T1w FLASH and by 7 with T2w HASTE. With CAIPIRINHA and with HASTE, there were significantly less motion artefacts in nonsedated patients. In sedated patients, respiratory-triggered T1w imaging in general showed better image quality. However, satisfactory image quality was achieved with CAIPIRINHA in two sedated patients where respiratory triggering failed. In summary, fast scanning with CAIPIRINHA and HASTE presents a reliable high quality alternative to standard sequences in paediatric abdominal MRI. Paediatric patients, in particular, benefit greatly from fast image acquisition with less breath-hold cycles or shorter sedation.

Diagnostic evaluation of the MRP-8/14 for the emergency assessment of chest pain.

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Vora, Amit N; Bonaca, Marc P; Ruff, Christian T; Jarolim, Petr; Murphy, Sabina; Croce, Kevin; Sabatine, Marc S; Simon, Daniel I; Morrow, David A

2012-08-01

Elevated levels of myeloid-related protein (MRP)-8/14 (S100A8/A9) are associated with first cardiovascular events in healthy individuals and worse prognosis in patients with acute coronary syndrome (ACS). The diagnostic utility of MRP-8/14 in patients presenting to the emergency room with symptoms concerning for ACS is uncertain. MRP-8/14 was measured in serial serum and plasma samples in a single center prospective cohort-study of patients presenting to the emergency room with non-traumatic chest pain concerning for ACS. Final diagnosis was adjudicated by an endpoint committee. Of patients with baseline MRP-8/14 results (n = 411), the median concentration in serum was 1.57 μg/ml (25th, 75th: 0.87, 2.68) and in plasma was 0.41 μg/ml (MRP-8/14 was higher in patients presenting with MI (p MRP-8/14 was poor: sensitivity 28% (95% CI 20-38), specificity 82% (78-86), positive predictive value 36% (26-47), and negative predictive value 77% (72-81). The area under the ROC curve for diagnosis of MI with MRP-8/14 was 0.55 (95% CI 0.51-0.60) compared with 0.95 for cTnI. The diagnostic performance was not improved in early-presenters, patients with negative initial cTnI, or using later MRP-8/14 samples. Patients presenting with MI had elevated levels of serum MRP-8/14 compared to patients with non-cardiac chest pain. However, overall diagnostic performance of MRP-8/14 was poor and neither plasma nor serum MRP-8/14 offered diagnostic utility comparable to cardiac troponin.

Typification of Zaluzianskya villosa F. W. Schmidt (Scrophulariaceae-Manuleae)

Czech Academy of Sciences Publication Activity Database

Kirschner, Jan

2009-01-01

Roč. 75, č. 3 (2009), s. 588-590 ISSN 0254-6299 R&D Projects: GA MŠk LC06073 Institutional research plan: CEZ:AV0Z60050516 Keywords : F. W. Schmidt * herbarium PRC * nomenclature Subject RIV: EF - Botanics Impact factor: 1.080, year: 2009

CMOS Image Sensor and System for Imaging Hemodynamic Changes in Response to Deep Brain Stimulation.

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Zhang, Xiao; Noor, Muhammad S; McCracken, Clinton B; Kiss, Zelma H T; Yadid-Pecht, Orly; Murari, Kartikeya

2016-06-01

Deep brain stimulation (DBS) is a therapeutic intervention used for a variety of neurological and psychiatric disorders, but its mechanism of action is not well understood. It is known that DBS modulates neural activity which changes metabolic demands and thus the cerebral circulation state. However, it is unclear whether there are correlations between electrophysiological, hemodynamic and behavioral changes and whether they have any implications for clinical benefits. In order to investigate these questions, we present a miniaturized system for spectroscopic imaging of brain hemodynamics. The system consists of a 144 ×144, [Formula: see text] pixel pitch, high-sensitivity, analog-output CMOS imager fabricated in a standard 0.35 μm CMOS process, along with a miniaturized imaging system comprising illumination, focusing, analog-to-digital conversion and μSD card based data storage. This enables stand alone operation without a computer, nor electrical or fiberoptic tethers. To achieve high sensitivity, the pixel uses a capacitive transimpedance amplifier (CTIA). The nMOS transistors are in the pixel while pMOS transistors are column-parallel, resulting in a fill factor (FF) of 26%. Running at 60 fps and exposed to 470 nm light, the CMOS imager has a minimum detectable intensity of 2.3 nW/cm(2) , a maximum signal-to-noise ratio (SNR) of 49 dB at 2.45 μW/cm(2) leading to a dynamic range (DR) of 61 dB while consuming 167 μA from a 3.3 V supply. In anesthetized rats, the system was able to detect temporal, spatial and spectral hemodynamic changes in response to DBS.

33 CFR 165.814 - Security Zones; Captain of the Port Houston-Galveston Zone.

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2010-07-01

... Port Houston-Galveston Zone. 165.814 Section 165.814 Navigation and Navigable Waters COAST GUARD... § 165.814 Security Zones; Captain of the Port Houston-Galveston Zone. (a) Location. The following areas are designated as security zones: (1) Houston, Texas. The Houston Ship Channel and all associated...

On the Absolute Age of the Globular Cluster M92

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Di Cecco, A.; Becucci, R.; Bono, G.; Monelli, M.; Stetson, P. B.; Degl'Innocenti, S.; Prada Moroni, P. G.; Nonino, M.; Weiss, A.; Buonanno, R.; Calamida, A.; Caputo, F.; Corsi, C. E.; Ferraro, I.; Iannicola, G.; Pulone, L.; Romaniello, M.; Walker, A. R.

2010-09-01

We present precise and deep optical photometry of the globular M92. Data were collected in three different photometric systems: Sloan Digital Sky Survey (g‧, r‧, i‧, and z‧ MegaCam at CFHT), Johnson-Kron-Cousins (B, V, and I; various ground-based telescopes), and Advanced Camera for Surveys (ACS) Vegamag (F475W, F555W, and F814W; Hubble Space Telescope). Special attention was given to the photometric calibration, and the precision of the ground-based data is generally better than 0.01 mag. We computed a new set of α-enhanced evolutionary models accounting for the gravitational settling of heavy elements at fixed chemical composition ([α/Fe] = +0.3, [Fe/H] = -2.32 dex, and Y = 0.248). The isochrones—assuming the same true distance modulus (μ = 14.74 mag), the same reddening [E(B - V) = 0.025 ± 0.010 mag], and the same reddening law—account for the stellar distribution along the main sequence and the red giant branch in different color-magnitude diagrams (i‧, g‧ - i‧ i‧, and g‧ - r‧ i‧, g‧ - z‧ I, and B - I and F814W and F475W-F814W). The same outcome applies to the comparison between the predicted zero-age horizontal-branch (ZAHB) and the HB stars. We also found a cluster age of 11 ± 1.5 Gyr, in good agreement with previous estimates. The error budget accounts for uncertainties in the input physics and the photometry. To test the possible occurrence of CNO-enhanced stars, we also computed two sets of α- and CNO-enhanced (by a factor of 3) models, both at fixed total metallicity ([M/H] = -2.10 dex) and at fixed iron abundance. We found that the isochrones based on the former set give the same cluster age (11 ± 1.5 Gyr) as the canonical α-enhanced isochrones. The isochrones based on the latter set also give a similar cluster age (10 ± 1.5 Gyr). These findings support previous results concerning the weak sensitivity of cluster isochrones to CNO-enhanced chemical mixtures. This paper makes use of data obtained from the Isaac Newton

Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions.

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Choi, Hongyoon

2018-04-01

Recent advances in deep learning have impacted various scientific and industrial fields. Due to the rapid application of deep learning in biomedical data, molecular imaging has also started to adopt this technique. In this regard, it is expected that deep learning will potentially affect the roles of molecular imaging experts as well as clinical decision making. This review firstly offers a basic overview of deep learning particularly for image data analysis to give knowledge to nuclear medicine physicians and researchers. Because of the unique characteristics and distinctive aims of various types of molecular imaging, deep learning applications can be different from other fields. In this context, the review deals with current perspectives of deep learning in molecular imaging particularly in terms of development of biomarkers. Finally, future challenges of deep learning application for molecular imaging and future roles of experts in molecular imaging will be discussed.

Distributed deep learning networks among institutions for medical imaging.

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Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

2018-03-29

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

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Lijuan Duan

2017-01-01

Full Text Available Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI, to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

Combat Development Study. Close Support Study Group 2 (CSSG 2). Volume 3. Main Report. Chapters 8-14 and Appendices A-G

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1980-02-01

I’T.J4 ~,/.’~tr- ,. __ .. FINAL RE. / .... V OLUME ýAIN REPORT . •.CHAPTERS 8-14 SAND / PPENDICES 64 1LIJ FeblI11IM80 DLAI RB UT I QN W 938 U.S...problems could become major problems. Personnel actions, such as pay, promotion, mail, etc., become acute when the FIST is located anywhere in the

A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.

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Janowczyk, Andrew; Doyle, Scott; Gilmore, Hannah; Madabhushi, Anant

2018-01-01

Deep learning (DL) has recently been successfully applied to a number of image analysis problems. However, DL approaches tend to be inefficient for segmentation on large image data, such as high-resolution digital pathology slide images. For example, typical breast biopsy images scanned at 40× magnification contain billions of pixels, of which usually only a small percentage belong to the class of interest. For a typical naïve deep learning scheme, parsing through and interrogating all the image pixels would represent hundreds if not thousands of hours of compute time using high performance computing environments. In this paper, we present a resolution adaptive deep hierarchical (RADHicaL) learning scheme wherein DL networks at lower resolutions are leveraged to determine if higher levels of magnification, and thus computation, are necessary to provide precise results. We evaluate our approach on a nuclear segmentation task with a cohort of 141 ER+ breast cancer images and show we can reduce computation time on average by about 85%. Expert annotations of 12,000 nuclei across these 141 images were employed for quantitative evaluation of RADHicaL. A head-to-head comparison with a naïve DL approach, operating solely at the highest magnification, yielded the following performance metrics: .9407 vs .9854 Detection Rate, .8218 vs .8489 F -score, .8061 vs .8364 true positive rate and .8822 vs 0.8932 positive predictive value. Our performance indices compare favourably with state of the art nuclear segmentation approaches for digital pathology images.

A survey on deep learning in medical image analysis.

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Litjens, Geert; Kooi, Thijs; Bejnordi, Babak Ehteshami; Setio, Arnaud Arindra Adiyoso; Ciompi, Francesco; Ghafoorian, Mohsen; van der Laak, Jeroen A W M; van Ginneken, Bram; Sánchez, Clara I

2017-12-01

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research. Copyright © 2017 Elsevier B.V. All rights reserved.

Highlights from the Future Earth Water-Energy-Food (W-E-F) Nexus Cluster Project Consultations

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Lawford, R. G.

2017-12-01

Future Earth launched its W-E-F Nexus project in 2015. The focus of the project was to explore how improved governance and integrated information systems could support sustainability in the W-E-F Nexus. Workshops were held in four regions of the world (North America, Europe, Eastern Asia, and Southern Africa) which facilitated a better understanding of the current role of information in decision-making within the W-E-F Nexus. In each of these workshops, needs and options for improving the provision of relevant integrated data and information to support decision-making were discussed. The workshops provided distinct perspectives on W-E-F issues for each region and each sector. Regional differences arise from climate, geomorphology, natural resources and existing infrastructure as well as the economic and social policies within each country. While the needs associated with this diversity are large, it is still possible to identify unifying themes and requirements for data and information which appeared very similar in all the regions. Important themes involve developing a common rigorous definition of the Nexus, ensuring the availability of data of all types are available in the scales, frequencies, and accuracies needed to support better decision making; and promoting the gathering, analysis and use of information to break down the silos associated with the three sectors are made. Information is also needed to monitor the effects of land ownership and land management on W-E-F issues, to maximize the efficiencies that can be realized from joint planning and increased coherence in the sectoral policy approaches to address climate and environmental issues. After commenting on these opportunities the presentation will outline possible elements of a research agenda for moving the W-E-F Nexus approach forward.

Discovery of a Supernova in HST imaging of the MACSJ0717 Frontier Field

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Rodney, Steven A.; Lotz, Jennifer; Strolger, Louis-Gregory

2013-10-01

We report the discovery of a supernova (SN) in Hubble Space Telescope (HST) observations centered on the galaxy cluster MACSJ0717. It was discovered in the F814W (i) band of the Advanced Camera for Surveys (ACS), in observations that were collected as part of the ongoing HST Frontier Fields (HFF) program (PI:J.Lotz, HST PID 13498). The FrontierSN ID for this object is SN HFF13Zar (nicknamed "SN Zara").

Deep kernel learning method for SAR image target recognition

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Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

Image quality assessment using deep convolutional networks

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Li, Yezhou; Ye, Xiang; Li, Yong

2017-12-01

This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.

Operational stability of a compact 600-W KrF laser

International Nuclear Information System (INIS)

Borisov, V M; Vinokhodov, A Yu; Vodchits, V A; El'tsov, A V; Basting, D; Stamm, U; Voss, F

1998-01-01

The problem of the operational stability of a KrF laser with an average output power of at least 600 W was investigated. An experimental study was made of the dependences of the rms deviation σ of the output energy on the charging voltage, on the pulse repetition rate, and on the operating time. The value of σ varied from 1.2% to 6.0%, depending on the experimental conditions. For an average power of ∼ 600 W, the deviation σ did not exceed 3.2%. (lasers and amplifiers)

Application of deep learning to the classification of images from colposcopy.

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Sato, Masakazu; Horie, Koji; Hara, Aki; Miyamoto, Yuichiro; Kurihara, Kazuko; Tomio, Kensuke; Yokota, Harushige

2018-03-01

The objective of the present study was to investigate whether deep learning could be applied successfully to the classification of images from colposcopy. For this purpose, a total of 158 patients who underwent conization were enrolled, and medical records and data from the gynecological oncology database were retrospectively reviewed. Deep learning was performed with the Keras neural network and TensorFlow libraries. Using preoperative images from colposcopy as the input data and deep learning technology, the patients were classified into three groups [severe dysplasia, carcinoma in situ (CIS) and invasive cancer (IC)]. A total of 485 images were obtained for the analysis, of which 142 images were of severe dysplasia (2.9 images/patient), 257 were of CIS (3.3 images/patient), and 86 were of IC (4.1 images/patient). Of these, 233 images were captured with a green filter, and the remaining 252 were captured without a green filter. Following the application of L2 regularization, L1 regularization, dropout and data augmentation, the accuracy of the validation dataset was ~50%. Although the present study is preliminary, the results indicated that deep learning may be applied to classify colposcopy images.

Photoacoustic image reconstruction via deep learning

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Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

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Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

Deep learning in medical imaging: General overview

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Lee, June Goo; Jun, Sang Hoon; Cho, Young Won; Lee, Hyun Na; KIm, Guk Bae; Seo, Joon Beom; Kim, Nam Kug [University of Ulsan College of Medicine, Asan Medical Center, Seoul (Korea, Republic of)

2017-08-01

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and health care, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

Deep learning in medical imaging: General overview

International Nuclear Information System (INIS)

Lee, June Goo; Jun, Sang Hoon; Cho, Young Won; Lee, Hyun Na; KIm, Guk Bae; Seo, Joon Beom; Kim, Nam Kug

2017-01-01

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and health care, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging

Deep Learning in Medical Imaging: General Overview

Science.gov (United States)

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae

2017-01-01

The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. PMID:28670152

Deep Learning in Medical Imaging: General Overview.

Science.gov (United States)

Lee, June-Goo; Jun, Sanghoon; Cho, Young-Won; Lee, Hyunna; Kim, Guk Bae; Seo, Joon Beom; Kim, Namkug

2017-01-01

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

THE ADVANCED CAMERA FOR SURVEYS NEARBY GALAXY SURVEY TREASURY. VI. THE ANCIENT STAR-FORMING DISK OF NGC 404

International Nuclear Information System (INIS)

Williams, Benjamin F.; Dalcanton, Julianne J.; Gilbert, Karoline M.; Stilp, Adrienne; Dolphin, Andrew; Seth, Anil C.; Weisz, Daniel; Skillman, Evan

2010-01-01

We present HST/WFPC2 observations across the disk of the nearby isolated dwarf S0 galaxy NGC 404, which hosts an extended gas disk. The locations of our fields contain a roughly equal mixture of bulge and disk stars. All of our resolved stellar photometry reaches m F814W = 26 (M F814W = -1.4), which covers 2.5 mag of the red giant branch and main-sequence stars with ages F814W = 27.2 (M F814W = -0.2), sufficient to resolve the red clump and main-sequence stars with ages 10 Gyr) population. Detailed modeling of the color-magnitude diagram suggests that ∼70% of the stellar mass in the NGC 404 disk formed by z ∼ 2 (10 Gyr ago) and at least ∼90% formed prior to z ∼ 1 (8 Gyr ago). These results indicate that the stellar populations of the NGC 404 disk are on average significantly older than those of other nearby disk galaxies, suggesting that early- and late-type disks may have different long-term evolutionary histories, not simply differences in their recent star formation rates. Comparisons of the spatial distribution of the young stellar mass and FUV emission in Galaxy Evolution Explorer images show that the brightest FUV regions contain the youngest stars, but that some young stars (<160 Myr) lie outside of these regions. FUV luminosity appears to be strongly affected by both age and stellar mass within individual regions. Finally, we use our measurements to infer the relationship between the star formation rate and the gas density of the disk at previous epochs. We find that most of the history of the NGC 404 disk is consistent with star formation that has decreased with the gas density according to the Schmidt law. However, ∼ 0.5-1 Gyr ago, the star formation rate was unusually low for the inferred gas density, consistent with the possibility that there was a gas accretion event that reignited star formation ∼0.5 Gyr ago. Such an event could explain why this S0 galaxy hosts an extended gas disk.

Study of CT image texture using deep learning techniques

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Dutta, Sandeep; Fan, Jiahua; Chevalier, David

2018-03-01

For CT imaging, reduction of radiation dose while improving or maintaining image quality (IQ) is currently a very active research and development topic. Iterative Reconstruction (IR) approaches have been suggested to be able to offer better IQ to dose ratio compared to the conventional Filtered Back Projection (FBP) reconstruction. However, it has been widely reported that often CT image texture from IR is different compared to that from FBP. Researchers have proposed different figure of metrics to quantitate the texture from different reconstruction methods. But there is still a lack of practical and robust method in the field for texture description. This work applied deep learning method for CT image texture study. Multiple dose scans of a 20cm diameter cylindrical water phantom was performed on Revolution CT scanner (GE Healthcare, Waukesha) and the images were reconstructed with FBP and four different IR reconstruction settings. The training images generated were randomly allotted (80:20) to a training and validation set. An independent test set of 256-512 images/class were collected with the same scan and reconstruction settings. Multiple deep learning (DL) networks with Convolution, RELU activation, max-pooling, fully-connected, global average pooling and softmax activation layers were investigated. Impact of different image patch size for training was investigated. Original pixel data as well as normalized image data were evaluated. DL models were reliably able to classify CT image texture with accuracy up to 99%. Results show that the deep learning techniques suggest that CT IR techniques may help lower the radiation dose compared to FBP.

Deep UV Native Fluorescence Imaging of Antarctic Cryptoendolithic Communities

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Storrie-Lombardi, M. C.; Douglas, S.; Sun, H.; McDonald, G. D.; Bhartia, R.; Nealson, K. H.; Hug, W. F.

2001-01-01

An interdisciplinary team at the Jet Propulsion Laboratory Center for Life Detection has embarked on a project to provide in situ chemical and morphological characterization of Antarctic cryptoendolithic microbial communities. We present here in situ deep ultraviolet (UV) native fluorescence and environmental scanning electron microscopy images transiting 8.5 mm into a sandstone sample from the Antarctic Dry Valleys. The deep ultraviolet imaging system employs 224.3, 248.6, and 325 nm lasers to elicit differential fluorescence and resonance Raman responses from biomolecules and minerals. The 224.3 and 248.6 nm lasers elicit a fluorescence response from the aromatic amino and nucleic acids. Excitation at 325 nm may elicit activity from a variety of biomolecules, but is more likely to elicit mineral fluorescence. The resultant fluorescence images provide in situ chemical and morphological maps of microorganisms and the associated organic matrix. Visible broadband reflectance images provide orientation against the mineral background. Environmental scanning electron micrographs provided detailed morphological information. The technique has made possible the construction of detailed fluorescent maps extending from the surface of an Antarctic sandstone sample to a depth of 8.5 mm. The images detect no evidence of microbial life in the superficial 0.2 mm crustal layer. The black lichen component between 0.3 and 0.5 mm deep absorbs all wavelengths of both laser and broadband illumination. Filamentous deep ultraviolet native fluorescent activity dominates in the white layer between 0.6 mm and 5.0 mm from the surface. These filamentous forms are fungi that continue into the red (iron-rich) region of the sample extending from 5.0 to 8.5 mm. Using differential image subtraction techniques it is possible to identify fungal nuclei. The ultraviolet response is markedly attenuated in this region, apparently from the absorption of ultraviolet light by iron-rich particles coating

Classification of radiolarian images with hand-crafted and deep features

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Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

2017-12-01

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

An Ensemble of Deep Support Vector Machines for Image Categorization

NARCIS (Netherlands)

Abdullah, Azizi; Veltkamp, Remco C.; Wiering, Marco

2009-01-01

This paper presents the deep support vector machine (D-SVM) inspired by the increasing popularity of deep belief networks for image recognition. Our deep SVM trains an SVM in the standard way and then uses the kernel activations of support vectors as inputs for training another SVM at the next

A novel biomedical image indexing and retrieval system via deep preference learning.

Science.gov (United States)

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state

34 CFR 300.814 - Other State-level activities.

Science.gov (United States)

2010-07-01

... CHILDREN WITH DISABILITIES Preschool Grants for Children with Disabilities § 300.814 Other State-level..., language, and numeracy skills) in accordance with Part C of the Act to children with disabilities who are...

50 CFR 81.4 - Allocation of funds.

Science.gov (United States)

2010-10-01

...) FINANCIAL ASSISTANCE-WILDLIFE SPORT FISH RESTORATION PROGRAM CONSERVATION OF ENDANGERED AND THREATENED SPECIES OF FISH, WILDLIFE, AND PLANTS-COOPERATION WITH THE STATES § 81.4 Allocation of funds. The... commitments of the United States to protect endangered or threatened species; (b) The readiness of a State to...

Intelligent Detection of Structure from Remote Sensing Images Based on Deep Learning Method

Science.gov (United States)

Xin, L.

2018-04-01

Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

Imaging findings and significance of deep neck space infection

International Nuclear Information System (INIS)

Zhuang Qixin; Gu Yifeng; Du Lianjun; Zhu Lili; Pan Yuping; Li Minghua; Yang Shixun; Shang Kezhong; Yin Shankai

2004-01-01

Objective: To study the imaging appearance of deep neck space cellulitis and abscess and to evaluate the diagnostic criteria of deep neck space infection. Methods: CT and MRI findings of 28 cases with deep neck space infection proved by clinical manifestation and pathology were analyzed, including 11 cases of retropharyngeal space, 5 cases of parapharyngeal space infection, 4 cases of masticator space infection, and 8 cases of multi-space infection. Results: CT and MRI could display the swelling of the soft tissues and displacement, reduction, or disappearance of lipoid space in the cellulitis. In inflammatory tissues, MRI imaging demonstrated hypointense or isointense signal on T 1 WI, and hyperintense signal changes on T 2 WI. In abscess, CT could display hypodensity in the center and boundary enhancement of the abscess. MRI could display obvious hyperintense signal on T 2 WI and boundary enhancement. Conclusion: CT and MRI could provide useful information for deep neck space cellulitis and abscess

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.

Science.gov (United States)

Xu, Lina; Tetteh, Giles; Lipkova, Jana; Zhao, Yu; Li, Hongwei; Christ, Patrick; Piraud, Marie; Buck, Andreas; Shi, Kuangyu; Menze, Bjoern H

2018-01-01

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68 Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68 Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68 Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k -Nearest Neighbors ( k -NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

Directory of Open Access Journals (Sweden)

Lina Xu

2018-01-01

Full Text Available The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM. 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs, V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF, k-Nearest Neighbors (k-NN, and support vector machine (SVM. The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.

W-transform method for feature-oriented multiresolution image retrieval

Energy Technology Data Exchange (ETDEWEB)

Kwong, M.K.; Lin, B. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

1995-07-01

Image database management is important in the development of multimedia technology. Since an enormous amount of digital images is likely to be generated within the next few decades in order to integrate computers, television, VCR, cables, telephone and various imaging devices. Effective image indexing and retrieval systems are urgently needed so that images can be easily organized, searched, transmitted, and presented. Here, the authors present a local-feature-oriented image indexing and retrieval method based on Kwong, and Tang`s W-transform. Multiresolution histogram comparison is an effective method for content-based image indexing and retrieval. However, most recent approaches perform multiresolution analysis for whole images but do not exploit the local features present in the images. Since W-transform is featured by its ability to handle images of arbitrary size, with no periodicity assumptions, it provides a natural tool for analyzing local image features and building indexing systems based on such features. In this approach, the histograms of the local features of images are used in the indexing, system. The system not only can retrieve images that are similar or identical to the query images but also can retrieve images that contain features specified in the query images, even if the retrieved images as a whole might be very different from the query images. The local-feature-oriented method also provides a speed advantage over the global multiresolution histogram comparison method. The feature-oriented approach is expected to be applicable in managing large-scale image systems such as video databases and medical image databases.

A Hubble Space Telescope survey for novae in M87 - III. Are novae good standard candles 15 d after maximum brightness?

Science.gov (United States)

Shara, Michael M.; Doyle, Trisha F.; Pagnotta, Ashley; Garland, James T.; Lauer, Tod R.; Zurek, David; Baltz, Edward A.; Goerl, Ariel; Kovetz, Attay; Machac, Tamara; Madrid, Juan P.; Mikołajewska, Joanna; Neill, J. D.; Prialnik, Dina; Welch, D. L.; Yaron, Ofer

2018-02-01

Ten weeks of daily imaging of the giant elliptical galaxy M87 with the Hubble Space Telescope (HST) has yielded 41 nova light curves of unprecedented quality for extragalactic cataclysmic variables. We have recently used these light curves to demonstrate that the observational scatter in the so-called maximum-magnitude rate of decline (MMRD) relation for classical novae is so large as to render the nova-MMRD useless as a standard candle. Here, we demonstrate that a modified Buscombe-de Vaucouleurs hypothesis, namely that novae with decline times t2 > 10 d converge to nearly the same absolute magnitude about two weeks after maximum light in a giant elliptical galaxy, is supported by our M87 nova data. For 13 novae with daily sampled light curves, well determined times of maximum light in both the F606W and F814W filters, and decline times t2 > 10 d we find that M87 novae display M606W,15 = -6.37 ± 0.46 and M814W,15 = -6.11 ± 0.43. If very fast novae with decline times t2 < 10 d are excluded, the distances to novae in elliptical galaxies with stellar binary populations similar to those of M87 should be determinable with 1σ accuracies of ± 20 per cent with the above calibrations.

The gridding method for image reconstruction by Fourier transformation

International Nuclear Information System (INIS)

Schomberg, H.; Timmer, J.

1995-01-01

This paper explores a computational method for reconstructing an n-dimensional signal f from a sampled version of its Fourier transform f. The method involves a window function w and proceeds in three steps. First, the convolution g = w * f is computed numerically on a Cartesian grid, using the available samples of f. Then, g = wf is computed via the inverse discrete Fourier transform, and finally f is obtained as g/w. Due to the smoothing effect of the convolution, evaluating w * f is much less error prone than merely interpolating f. The method was originally devised for image reconstruction in radio astronomy, but is actually applicable to a broad range of reconstructive imaging methods, including magnetic resonance imaging and computed tomography. In particular, it provides a fast and accurate alternative to the filtered backprojection. The basic method has several variants with other applications, such as the equidistant resampling of arbitrarily sampled signals or the fast computation of the Radon (Hough) transform

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.

Science.gov (United States)

Janowczyk, Andrew; Madabhushi, Anant

2016-01-01

Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific "handcrafted" features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Specifically, in this tutorial on DL for DP image

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

Directory of Open Access Journals (Sweden)

Andrew Janowczyk

2016-01-01

Full Text Available Background: Deep learning (DL is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP. The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events, segmentation (e.g., nuclei, and tissue classification (e.g., cancerous vs. non-cancerous. Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific "handcrafted" features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a selecting appropriate magnification, (b managing errors in annotations in the training (or learning dataset, and (c identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i DL experts with minimal digital histology experience, and (ii DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. Aims: This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Results : Specifically, in

Exacerbating effects of human parvovirus B19 NS1 on liver fibrosis in NZB/W F1 mice.

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Tsai-Ching Hsu

Full Text Available Systemic lupus erythematosus (SLE is an autoimmune disorder with unknown etiology that impacts various organs including liver. Recently, human parvovirus B19 (B19 is recognized to exacerbate SLE. However, the effects of B19 on liver in SLE are still unclear. Herein we aimed to investigate the effects of B19 on liver in NZB/W F1 mice by injecting subcutaneously with PBS, recombinant B19 NS1, VP1u or VP2, respectively. Our experimental results revealed that B19 NS1 protein significantly enhanced the TGF-β/Smad fibrotic signaling by increasing the expressions of TGF-β, Smad2/3, phosphorylated Smad2/3, Smad4 and Sp1. The consequent fibrosis-related proteins, PAI-1 and α-SMA, were also significantly induced in livers of NZB/W F1 mice receiving B19 NS1 protein. Accordingly, markedly increased collagen deposition was also observed in livers of NZB/W F1 mice receiving B19 NS1 protein. However, no significant difference was observed in livers of NZB/W F1 mice receiving B19 VP1u or VP2 as compared to the controls. These findings indicate that B19 NS1 plays a crucial role in exacerbating liver fibrosis in NZB/W F1 mice through enhancing the TGF-â/Smad fibrotic signaling.

Exacerbating Effects of Human Parvovirus B19 NS1 on Liver Fibrosis in NZB/W F1 Mice

Science.gov (United States)

Hsu, Tsai-Ching; Tsai, Chun-Chou; Chiu, Chun-Ching; Hsu, Jeng-Dong; Tzang, Bor-Show

2013-01-01

Systemic lupus erythematosus (SLE) is an autoimmune disorder with unknown etiology that impacts various organs including liver. Recently, human parvovirus B19 (B19) is recognized to exacerbate SLE. However, the effects of B19 on liver in SLE are still unclear. Herein we aimed to investigate the effects of B19 on liver in NZB/W F1 mice by injecting subcutaneously with PBS, recombinant B19 NS1, VP1u or VP2, respectively. Our experimental results revealed that B19 NS1 protein significantly enhanced the TGF-β/Smad fibrotic signaling by increasing the expressions of TGF-β, Smad2/3, phosphorylated Smad2/3, Smad4 and Sp1. The consequent fibrosis-related proteins, PAI-1 and α-SMA, were also significantly induced in livers of NZB/W F1 mice receiving B19 NS1 protein. Accordingly, markedly increased collagen deposition was also observed in livers of NZB/W F1 mice receiving B19 NS1 protein. However, no significant difference was observed in livers of NZB/W F1 mice receiving B19 VP1u or VP2 as compared to the controls. These findings indicate that B19 NS1 plays a crucial role in exacerbating liver fibrosis in NZB/W F1 mice through enhancing the TGF-â/Smad fibrotic signaling. PMID:23840852

Deep sequencing reveals double mutations in cis of MPL exon 10 in myeloproliferative neoplasms.

Science.gov (United States)

Pietra, Daniela; Brisci, Angela; Rumi, Elisa; Boggi, Sabrina; Elena, Chiara; Pietrelli, Alessandro; Bordoni, Roberta; Ferrari, Maurizio; Passamonti, Francesco; De Bellis, Gianluca; Cremonesi, Laura; Cazzola, Mario

2011-04-01

Somatic mutations of MPL exon 10, mainly involving a W515 substitution, have been described in JAK2 (V617F)-negative patients with essential thrombocythemia and primary myelofibrosis. We used direct sequencing and high-resolution melt analysis to identify mutations of MPL exon 10 in 570 patients with myeloproliferative neoplasms, and allele specific PCR and deep sequencing to further characterize a subset of mutated patients. Somatic mutations were detected in 33 of 221 patients (15%) with JAK2 (V617F)-negative essential thrombocythemia or primary myelofibrosis. Only one patient with essential thrombocythemia carried both JAK2 (V617F) and MPL (W515L). High-resolution melt analysis identified abnormal patterns in all the MPL mutated cases, while direct sequencing did not detect the mutant MPL in one fifth of them. In 3 cases carrying double MPL mutations, deep sequencing analysis showed identical load and location in cis of the paired lesions, indicating their simultaneous occurrence on the same chromosome.

Usability as the Key Factor to the Design of a Web Server for the CReF Protein Structure Predictor: The wCReF

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Vanessa Stangherlin Machado Paixão-Cortes

2018-01-01

Full Text Available Protein structure prediction servers use various computational methods to predict the three-dimensional structure of proteins from their amino acid sequence. Predicted models are used to infer protein function and guide experimental efforts. This can contribute to solving the problem of predicting tertiary protein structures, one of the main unsolved problems in bioinformatics. The challenge is to understand the relationship between the amino acid sequence of a protein and its three-dimensional structure, which is related to the function of these macromolecules. This article is an extended version of the article wCReF: The Web Server for the Central Residue Fragment-based Method (CReF Protein Structure Predictor, published in the 14th International Conference on Information Technology: New Generations. In the first version, we presented the wCReF, a protein structure prediction server for the central residue fragment-based method. The wCReF interface was developed with a focus on usability and user interaction. With this tool, users can enter the amino acid sequence of their target protein and obtain its approximate 3D structure without the need to install all the multitude of necessary tools. In this extended version, we present the design process of the prediction server in detail, which includes: (A identification of user needs: aiming at understanding the features of a protein structure prediction server, the end user profiles and the commonly-performed tasks; (B server usability inspection: in order to define wCReF’s requirements and features, we have used heuristic evaluation guided by experts in both the human-computer interaction and bioinformatics domain areas, applied to the protein structure prediction servers I-TASSER, QUARK and Robetta; as a result, changes were found in all heuristics resulting in 89 usability problems; (C software requirements document and prototype: assessment results guiding the key features that wCReF must

Deep multi-scale convolutional neural network for hyperspectral image classification

Science.gov (United States)

Zhang, Feng-zhe; Yang, Xia

2018-04-01

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

LUMINOUS AND HIGH STELLAR MASS CANDIDATE GALAXIES AT z ≈ 8 DISCOVERED IN THE COSMIC ASSEMBLY NEAR-INFRARED DEEP EXTRAGALACTIC LEGACY SURVEY

International Nuclear Information System (INIS)

Yan Haojing; Finkelstein, Steven L.; Huang, Kuang-Han; Ryan, Russell E.; Ferguson, Henry C.; Koekemoer, Anton M.; Grogin, Norman A.; Dickinson, Mark; Newman, Jeffrey A.; Somerville, Rachel S.; Davé, Romeel; Faber, S. M.; Papovich, Casey; Guo Yicheng; Giavalisco, Mauro; Lee, Kyoung-soo; Reddy, Naveen; Siana, Brian D.; Cooray, Asantha R.; Hathi, Nimish P.

2012-01-01

One key goal of the Hubble Space Telescope Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey is to track galaxy evolution back to z ≈ 8. Its two-tiered ''wide and deep'' strategy bridges significant gaps in existing near-infrared surveys. Here we report on z ≈ 8 galaxy candidates selected as F105W-band dropouts in one of its deep fields, which covers 50.1 arcmin 2 to 4 ks depth in each of three near-infrared bands in the Great Observatories Origins Deep Survey southern field. Two of our candidates have J 1 mag brighter than any previously known F105W-dropouts. We derive constraints on the bright end of the rest-frame ultraviolet luminosity function of galaxies at z ≈ 8, and show that the number density of such very bright objects is higher than expected from the previous Schechter luminosity function estimates at this redshift. Another two candidates are securely detected in Spitzer Infrared Array Camera images, which are the first such individual detections at z ≈ 8. Their derived stellar masses are on the order of a few × 10 9 M ☉ , from which we obtain the first measurement of the high-mass end of the galaxy stellar mass function at z ≈ 8. The high number density of very luminous and very massive galaxies at z ≈ 8, if real, could imply a large stellar-to-halo mass ratio and an efficient conversion of baryons to stars at such an early time.

Human Parvovirus B19 NS1 Protein Aggravates Liver Injury in NZB/W F1 Mice

Science.gov (United States)

Tsai, Chun-Chou; Chiu, Chun-Ching; Hsu, Jeng-Dong; Hsu, Huai-Sheng; Tzang, Bor-Show; Hsu, Tsai-Ching

2013-01-01

Human parvovirus B19 (B19) has been associated with a variety of diseases. However, the influence of B19 viral proteins on hepatic injury in SLE is still obscure. To elucidate the effects of B19 viral proteins on livers in SLE, recombinant B19 NS1, VP1u or VP2 proteins were injected subcutaneously into NZB/W F1 mice, respectively. Significant expressions of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) were detected in NZB/W F1 mice receiving B19 NS1 as compared to those mice receiving PBS. Markedly hepatocyte disarray and lymphocyte infiltration were observed in livers from NZB/WF 1 mice receiving B19 NS1 as compared to those mice receiving PBS. Additionally, significant increases of Tumor Necrosis Factor –α (TNF-α), TNF-α receptor, IκB kinase –α (IKK-α), nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor (IκB) and nuclear factor-kappa B (NF-κB) were detected in livers from NZB/W F1 mice receiving B19 NS1 as compared to those mice receiving PBS. Accordingly, significant increases of matrix metalloproteinase-9 (MMP9) and U-plasminogen activator (uPA) were also detected in livers from NZB/W F1 mice receiving B19 NS1 as compared to those mice receiving PBS. Contrarily, no significant variation on livers from NZB/W F1 mice receiving B19 VP1u or VP2 was observed as compared to those mice receiving PBS. These findings firstly demonstrated the aggravated effects of B19 NS1 but not VP1u or VP2 protein on hepatic injury and provide a clue in understanding the role of B19 NS1 on hepatic injury in SLE. PMID:23555760

Deep learning for tumor classification in imaging mass spectrometry.

Science.gov (United States)

Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

2018-04-01

Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

Naval Aviation: F-14 Upgrades are not Adequately Justified

Science.gov (United States)

1994-10-01

1AMI aircraf wdll li4c( h~ave1 a radar PigUnI 3i4 npiJ814 capal~ ihq i) tamvai4 cro-w’ tit lm-uai:g_ ,e1.’nitiyirng. anti ;Uatimkitg taggetj6 whens...CaiafornrM Weq conducted (put rrvi-ew between June 16W9 andl MA) lW.14 in accoriaaice with g.enerall) auc"- td giwenhzne~nt aiuiitlZg ntaandard We. are

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Directory of Open Access Journals (Sweden)

Fan Zhang

2016-04-01

Full Text Available With the development of synthetic aperture radar (SAR technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO. However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

Science.gov (United States)

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

Science.gov (United States)

Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

2017-01-15

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the

[Serum levels of myeloid-related protein MRP 8/14 (calprotectin) in Armenian patients with familial mediterranean fever].

Science.gov (United States)

Dzhndoian, Z T

2012-01-01

The determination of serum myeloid-related protein MRP 8/14 (calprotectin) in Armenian patients with FMF before and after treatment with colchicine (including colchicine-resistant patients who don't respond to 2 mg of colchicine; t patients who don't respond to 1,5 mg of colchicine, and also responders to different dose of colchicine) and estimation of the response to antiinflammatory therapy. MRP 8/14 serum levels were measured in 80 FMF patients before and after treatment with colchicine and in healthy individuals (n = 11) and patients with rheumatoid arthritis RA (n=11) as a control group. Serum MRP 8/14 concentration was measured by ELISA (Enzyme Linked-Immuno-Sorbent-Assay) method using "Buhlmann" kit (Switzerland) in the laboratory with modern equipment. Serum MRP 8/14 concentrations were within a normal ranges in healthy individuals and elevated in patients with FMF and RA. MRP 8/14 serum levels in FMF patients were higher than in RA patients. Serum MRP 8/14 concentrations in FMF patients before colchicines therapy were higher than after treatment. The findings of our study indicate that myeloid-related protein MRP 8/14 is a very sensitive marker of the disease activity and response to antiinflammatory therapy in FMF.

Down image recognition based on deep convolutional neural network

Directory of Open Access Journals (Sweden)

Wenzhu Yang

2018-06-01

Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition

Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Science.gov (United States)

Xue, Yong; Chen, Shihui; Liu, Yong

2017-01-01

Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection of cancer. In recent years, deep leaning has been widely used in medical imaging analysis, as it overcomes the limitations of visual assessment and traditional machine learning techniques by extracting hierarchical features with powerful representation capability. Research on cancer molecular images using deep learning techniques is also increasing dynamically. Hence, in this paper, we review the applications of deep learning in molecular imaging in terms of tumor lesion segmentation, tumor classification, and survival prediction. We also outline some future directions in which researchers may develop more powerful deep learning models for better performance in the applications in cancer molecular imaging. PMID:29114182

Studies of relativistic heavy ion collisions at the AGS (Experiment 814)

International Nuclear Information System (INIS)

Cleland, W.E.

1992-01-01

During the past year, the Pittsburgh group has continued to work with the E814 collaboration in carrying out AGS Experiment 814. We present here a brief history of the experiment, followed by a detailed report of the analysis work being pursued at the University of Pittsburgh. As originally proposed, Experiment 814 is a study of both extreme peripheral collisions and the transition from peripheral to central collisions in relativistic heavy ion-nucleus interactions. We are studying relativistic heavy ion interactions with nuclei in two types of collisions: (a) extreme peripheral collisions of large impact parameter, and (b) central collisions with high transverse energy in the final state. The experiment emphasizes the measurement of overall event characteristics, in particular energy flow measurements and a precise measurement of the particle charge, momentum, and energy in the forward direction. This permits measurements of cross sections and rapidity densities as a function of the transverse energy for leading baryons emitted into regions of larger rapidity. Combining the energy flow measurements as a function of rapidity with the spectra of leading baryons provides information on the impact parameter dependence of the nuclear stopping of the projectile in relativistic heavy ion collisions. In 1988, the scope of Experiment 814 was enlarged to include a search for strange matter in central collisions, the first results of which have been published, and analysis on a longer run taken in 1990 is still under way

Blind CT image quality assessment via deep learning strategy: initial study

Science.gov (United States)

Li, Sui; He, Ji; Wang, Yongbo; Liao, Yuting; Zeng, Dong; Bian, Zhaoying; Ma, Jianhua

2018-03-01

Computed Tomography (CT) is one of the most important medical imaging modality. CT images can be used to assist in the detection and diagnosis of lesions and to facilitate follow-up treatment. However, CT images are vulnerable to noise. Actually, there are two major source intrinsically causing the CT data noise, i.e., the X-ray photo statistics and the electronic noise background. Therefore, it is necessary to doing image quality assessment (IQA) in CT imaging before diagnosis and treatment. Most of existing CT images IQA methods are based on human observer study. However, these methods are impractical in clinical for their complex and time-consuming. In this paper, we presented a blind CT image quality assessment via deep learning strategy. A database of 1500 CT images is constructed, containing 300 high-quality images and 1200 corresponding noisy images. Specifically, the high-quality images were used to simulate the corresponding noisy images at four different doses. Then, the images are scored by the experienced radiologists by the following attributes: image noise, artifacts, edge and structure, overall image quality, and tumor size and boundary estimation with five-point scale. We trained a network for learning the non-liner map from CT images to subjective evaluation scores. Then, we load the pre-trained model to yield predicted score from the test image. To demonstrate the performance of the deep learning network in IQA, correlation coefficients: Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are utilized. And the experimental result demonstrate that the presented deep learning based IQA strategy can be used in the CT image quality assessment.

Observations of the Hubble Deep Field with the Infrared Space Observatory .4. Association of sources with Hubble Deep Field galaxies

DEFF Research Database (Denmark)

Mann, R.G.; Oliver, S.J.; Serjeant, S.B.G.

1997-01-01

We discuss the identification of sources detected by the Infrared Space Observatory (ISO) at 6.7 and 15 mu m in the Hubble Deep Field (HDF) region. We conservatively associate ISO sources with objects in existing optical and near-infrared HDF catalogues using the likelihood ratio method, confirming...... these results (and, in one case, clarifying them) with independent visual searches, We find 15 ISO sources to be reliably associated with bright [I-814(AB) HDF, and one with an I-814(AB)=19.9 star, while a further 11 are associated with objects in the Hubble Flanking Fields (10 galaxies...... and one star), Amongst optically bright HDF galaxies, ISO tends to detect luminous, star-forming galaxies at fairly high redshift and with disturbed morphologies, in preference to nearby ellipticals....

JAK2 V617F, MPL W515L and JAK2 Exon 12 Mutations in Chinese Patients with Primary Myelofibrosis.

Science.gov (United States)

Xia, Jun; Lu, Mi-Ze; Jiang, Yuan-Qiang; Yang, Guo-Hua; Zhuang, Yun; Sun, Hong-Li; Shen, Yun-Feng

2012-03-01

JAK2 V617F, MPL W515L and JAK2 exon 12 mutations are novel acquired mutations that induce constitutive cytokine-independent activation of the JAK-STAT pathway in myeloproliferative disorders (MPD). The discovery of these mutations provides novel mechanism for activation of signal transduction in hematopoietic malignancies. This research was to investigate their prevalence in Chinese patients with primary myelofibrosis (PMF). We introduced allele-specific PCR (AS-PCR) combined with sequence analysis to simultaneously screen JAK2 V617F, MPL W515L and JAK2 exon 12 mutations in 30 patients with PMF. Fifteen PMF patients (50.0%) carried JAK2 V617F mutation, and only two JAK2 V617F-negative patients (6.7%) harbored MPL W515L mutation. None had JAK2 exon 12 mutations. Furthermore, these three mutations were not detected in 50 healthy controls. MPL W515L and JAK2 V617F mutations existed in PMF patients but JAK2 exon 12 mutations not. JAK2 V617F and MPL W515L and mutations might contribute to the primary molecular pathogenesis in patients with PMF.

NaGdF4:Nd3+/Yb3+ Nanoparticles as Multimodal Imaging Agents

Science.gov (United States)

Pedraza, Francisco; Rightsell, Chris; Kumar, Ga; Giuliani, Jason; Monton, Car; Sardar, Dhiraj

Medical imaging is a fundamental tool used for the diagnosis of numerous ailments. Each imaging modality has unique advantages; however, they possess intrinsic limitations. Some of which include low spatial resolution, sensitivity, penetration depth, and radiation damage. To circumvent this problem, the combination of imaging modalities, or multimodal imaging, has been proposed, such as Near Infrared Fluorescence imaging (NIRF) and Magnetic Resonance Imaging (MRI). Combining individual advantages, specificity and selectivity of NIRF with the deep penetration and high spatial resolution of MRI, it is possible to circumvent their shortcomings for a more robust imaging technique. In addition, both imaging modalities are very safe and minimally invasive. Fluorescent nanoparticles, such as NaGdF4:Nd3 +/Yb3 +, are excellent candidates for NIRF/MRI multimodal imaging. The dopants, Nd and Yb, absorb and emit within the biological window; where near infrared light is less attenuated by soft tissue. This results in less tissue damage and deeper tissue penetration making it a viable candidate in biological imaging. In addition, the inclusion of Gd results in paramagnetic properties, allowing their use as contrast agents in multimodal imaging. The work presented will include crystallographic results, as well as full optical and magnetic characterization to determine the nanoparticle's viability in multimodal imaging.

Imaging malignant melanoma with {sup 18}F-5-FPN

Energy Technology Data Exchange (ETDEWEB)

Feng, Hongyan; Xia, Xiaotian; Li, Chongjiao; Song, Yiling; Qin, Chunxia; Liu, Qingyao; Zhang, Yongxue; Lan, Xiaoli [Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (China); Hubei Key Laboratory of Molecular Imaging (China)

2016-01-15

Radiolabelled benzamides are attractive candidates for targeting melanoma because they bind to melanin and exhibit high tumour uptake and retention. {sup 18}F-5-Fluoro-N-(2-[diethylamino]ethyl)picolinamide ({sup 18}F-5-FPN), a benzamide analogue, was prepared and its pharmacokinetics and binding affinity evaluated both in vitro and in vivo to assess its clinical potential in the diagnosis and staging of melanoma. {sup 18}F-5-FPN was prepared and purified. Its binding specificity was measured in vitro in two different melanoma cell lines, one pigmented (B16F10 cells) and one nonpigmented (A375m cells), and in vivo in mice xenografted with the same cell lines. Dynamic and static PET images using {sup 18}F-5-FPN were obtained in the tumour-bearing mice, and the static images were also compared with those acquired with {sup 18}F-FDG. PET imaging with {sup 18}F-5-FPN was also performed in B16F10 tumour-bearing mice with lung metastases. {sup 18}F-5-FPN was successfully prepared with radiochemical yields of 5 - 10 %. Binding of {sup 18}F-5-FPN to B16F10 cells was much higher than to A375m cells. On dynamic PET imaging B16F10 tumours were visible about 1 min after injection of the tracer, and the uptake gradually increased over time. {sup 18}F-5-FPN was rapidly excreted via the kidneys. B16F10 tumours were clearly visible on static images acquired 1 and 2 h after injection, with high uptake values of 24.34 ± 6.32 %ID/g and 16.63 ± 5.41 %ID/g, respectively, in the biodistribution study (five mice). However, there was no visible uptake by A375m tumours. {sup 18}F-5-FPN and {sup 18}F-FDG PET imaging were compared in B16F10 tumour xenografts, and the tumour-to-background ratio of {sup 18}F-5-FPN was ten times higher than that of {sup 18}F-FDG (35.22 ± 7.02 vs. 3.29 ± 0.53, five mice). {sup 18}F-5-FPN PET imaging also detected simulated lung metastases measuring 1 - 2 mm. {sup 18}F-5-FPN specifically targeted melanin in vitro and in vivo with high retention and affinity

THE GHOSTS SURVEY. I. HUBBLE SPACE TELESCOPE ADVANCED CAMERA FOR SURVEYS DATA

International Nuclear Information System (INIS)

Radburn-Smith, D. J.; Dalcanton, J. J.; De Jong, R. S.; Streich, D.; Vlajic, M.; Seth, A. C.; Bailin, J.; Bell, E. F.; Brown, T. M.; Ferguson, H. C.; Goudfrooij, P.; Holfeltz, S.; Bullock, J. S.; Courteau, S.; Sick, J.; Holwerda, B. W.; Purcell, C.; Zucker, D. B.

2011-01-01

We present an overview of the GHOSTS survey, the largest study to date of the resolved stellar populations in the outskirts of disk galaxies. The sample consists of 14 disk galaxies within 17 Mpc, whose outer disks and halos are imaged with the Hubble Space Telescope Advanced Camera for Surveys (ACS). In the first paper of this series, we describe the sample, explore the benefits of using resolved stellar populations, and discuss our ACS F606W and F814W photometry. We use artificial star tests to assess completeness and use overlapping regions to estimate photometric uncertainties. The median depth of the survey at 50% completeness is 2.7 mag below the tip of the red giant branch (TRGB). We comprehensively explore and parameterize contamination from unresolved background galaxies and foreground stars using archival fields of high-redshift ACS observations. Left uncorrected, these would account for 10 0.65xF814W-19.0 detections per mag per arcsec 2 . We therefore identify several selection criteria that typically remove 95% of the contaminants. Even with these culls, background galaxies are a significant limitation to the surface brightness detection limit which, for this survey, is typically V ∼ 30 mag arcsec -2 . The resulting photometric catalogs are publicly available and contain some 3.1 million stars across 76 ACS fields, predominantly of low extinction. The uniform magnitudes of TRGB stars in these fields enable galaxy distance estimates with 2%-7% accuracy.

Deep image mining for diabetic retinopathy screening.

Science.gov (United States)

Quellec, Gwenolé; Charrière, Katia; Boudi, Yassine; Cochener, Béatrice; Lamard, Mathieu

2017-07-01

Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions. In other words, a ConvNet trained for image-level classification can be used to detect lesions as well. A generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps. The proposed solution is applied to diabetic retinopathy (DR) screening in a dataset of almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy competition and a private dataset of almost 110,000 photographs (e-ophtha). For the task of detecting referable DR, very good detection performance was achieved: A z =0.954 in Kaggle's dataset and A z =0.949 in e-ophtha. Performance was also evaluated at the image level and at the lesion level in the DiaretDB1 dataset, where four types of lesions are manually segmented: microaneurysms, hemorrhages, exudates and cotton-wool spots. For the task of detecting images containing these four lesion types, the proposed detector, which was trained to detect referable DR, outperforms recent algorithms trained to detect those lesions specifically, with pixel-level supervision. At the lesion level, the proposed detector outperforms heatmap generation algorithms for ConvNets. This detector is part of the Messidor® system for mobile eye pathology screening. Because it does not rely on expert knowledge or manual segmentation for detecting relevant patterns, the proposed solution is a promising image

Applying Deep Learning in Medical Images: The Case of Bone Age Estimation.

Science.gov (United States)

Lee, Jang Hyung; Kim, Kwang Gi

2018-01-01

A diagnostic need often arises to estimate bone age from X-ray images of the hand of a subject during the growth period. Together with measured physical height, such information may be used as indicators for the height growth prognosis of the subject. We present a way to apply the deep learning technique to medical image analysis using hand bone age estimation as an example. Age estimation was formulated as a regression problem with hand X-ray images as input and estimated age as output. A set of hand X-ray images was used to form a training set with which a regression model was trained. An image preprocessing procedure is described which reduces image variations across data instances that are unrelated to age-wise variation. The use of Caffe, a deep learning tool is demonstrated. A rather simple deep learning network was adopted and trained for tutorial purpose. A test set distinct from the training set was formed to assess the validity of the approach. The measured mean absolute difference value was 18.9 months, and the concordance correlation coefficient was 0.78. It is shown that the proposed deep learning-based neural network can be used to estimate a subject's age from hand X-ray images, which eliminates the need for tedious atlas look-ups in clinical environments and should improve the time and cost efficiency of the estimation process.

42 CFR 431.814 - Sampling plan and procedures.

Science.gov (United States)

2010-10-01

... reliability of the reduced sample. (4) The sample selection procedure. Systematic random sampling is... sampling, and yield estimates with the same or better precision than achieved in systematic random sampling... 42 Public Health 4 2010-10-01 2010-10-01 false Sampling plan and procedures. 431.814 Section 431...

The MUSE Hubble Ultra Deep Field Survey. II. Spectroscopic redshifts and comparisons to color selections of high-redshift galaxies

Science.gov (United States)

Inami, H.; Bacon, R.; Brinchmann, J.; Richard, J.; Contini, T.; Conseil, S.; Hamer, S.; Akhlaghi, M.; Bouché, N.; Clément, B.; Desprez, G.; Drake, A. B.; Hashimoto, T.; Leclercq, F.; Maseda, M.; Michel-Dansac, L.; Paalvast, M.; Tresse, L.; Ventou, E.; Kollatschny, W.; Boogaard, L. A.; Finley, H.; Marino, R. A.; Schaye, J.; Wisotzki, L.

2017-11-01

We have conducted a two-layered spectroscopic survey (1' × 1' ultra deep and 3' × 3' deep regions) in the Hubble Ultra Deep Field (HUDF) with the Multi Unit Spectroscopic Explorer (MUSE). The combination of a large field of view, high sensitivity, and wide wavelength coverage provides an order of magnitude improvement in spectroscopically confirmed redshifts in the HUDF; i.e., 1206 secure spectroscopic redshifts for Hubble Space Telescope (HST) continuum selected objects, which corresponds to 15% of the total (7904). The redshift distribution extends well beyond z> 3 and to HST/F775W magnitudes as faint as ≈ 30 mag (AB, 1σ). In addition, 132 secure redshifts were obtained for sources with no HST counterparts that were discovered in the MUSE data cubes by a blind search for emission-line features. In total, we present 1338 high quality redshifts, which is a factor of eight increase compared with the previously known spectroscopic redshifts in the same field. We assessed redshifts mainly with the spectral features [O II] at zcolor selection (dropout) diagrams of high-z galaxies. The selection condition for F336W dropouts successfully captures ≈ 80% of the targeted z 2.7 galaxies. However, for higher redshift selections (F435W, F606W, and F775W dropouts), the success rates decrease to ≈ 20-40%. We empirically redefine the selection boundaries to make an attempt to improve them to ≈ 60%. The revised boundaries allow bluer colors that capture Lyα emitters with high Lyα equivalent widths falling in the broadbands used for the color-color selection. Along with this paper, we release the redshift and line flux catalog. Based on observations made with ESO telescopes at the La Silla Paranal Observatory under program IDs 094.A-0289(B), 095.A-0010(A), 096.A-0045(A) and 096.A-0045(B).MUSE Ultra Deep Field redshift catalogs (Full Table A.1) are available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http

Imaging Bone–Cartilage Interactions in Osteoarthritis Using [18F]-NaF PET-MRI

Directory of Open Access Journals (Sweden)

Dragana Savic MSc

2016-12-01

Full Text Available Purpose: Simultaneous positron emission tomography–magnetic resonance imaging (PET-MRI is an emerging technology providing both anatomical and functional images without increasing the scan time. Compared to the traditional PET/computed tomography imaging, it also exposes the patient to significantly less radiation and provides better anatomical images as MRI provides superior soft tissue characterization. Using PET-MRI, we aim to study interactions between cartilage composition and bone function simultaneously, in knee osteoarthritis (OA. Procedures: In this article, bone turnover and remodeling was studied using [18F]-sodium fluoride (NaF PET data. Quantitative MR-derived T1ρ relaxation times characterized the biochemical cartilage degeneration. Sixteen participants with early signs of OA of the knee received intravenous injections of [18F]-NaF at the onset of PET-MR image acquisition. Regions of interest were identified, and kinetic analysis of dynamic PET data provided the rate of uptake (Ki and the normalized uptake (standardized uptake value of [18F]-NaF in the bone. Morphological MR images and quantitative voxel-based T1ρ maps of cartilage were obtained using an atlas-based registration technique to segment cartilage automatically. Voxel-by-voxel statistical parameter mapping was used to investigate the relationship between bone and cartilage. Results: Increases in cartilage T1ρ, indicating degenerative changes, were associated with increased turnover in the adjoining bone but reduced turnover in the nonadjoining compartments. Associations between pain and increased bone uptake were seen in the absence of morphological lesions in cartilage, but the relationship was reversed in the presence of incident cartilage lesions. Conclusion: This study shows significant cartilage and bone interactions in OA of the knee joint using simultaneous [18F]-NaF PET-MR, the first in human study. These observations highlight the complex biomechanical and

Biodistribution and PET imaging of [18F]-fluoroadenosine derivatives

International Nuclear Information System (INIS)

Alauddin, Mian M.; Shahinian, Antranik; Park, Ryan; Tohme, Michael; Fissekis, John D.; Conti, Peter S.

2007-01-01

Introduction: Many fluorinated analogues of adenosine nucleoside have been synthesized and studied as potential antitumor and antiviral agents. Earlier, we reported radiosynthesis of 2'-deoxy-2'-[ 18 F]fluoro-1-β-D-arabinofuranosyl-adenine ([ 18 F]-FAA) and 3'-deoxy-3'-[ 18 F]fluoro-1-β-D-xylofuranosyl-adenine ([ 18 F]FXA). Now, we report their in vivo studies including blood clearance, biodistribution and micro-PET imaging in tumor-bearing nude mice. Methods: Tumors were grown in 6-week-old athymic nude mice (Harlan, Indianapolis, IN, USA) by inoculation of HT-29 cells, wild-type cells in the left flank and transduced cells with HSV-tk on the right flank. When the tumor was about 1 cm in size, animals were injected with these radiotracers for in vivo studies, including blood clearance, micro-PET imaging and biodistribution. Results: Uptake of [ 18 F]FAA in tumor was 3.3-fold higher than blood, with highest uptake in the spleen. Maximum uptake of [ 18 F]FXA was observed in the heart compared to other organs. There was no tumor uptake of [ 18 F]FXA. Biodistribution results were supported by micro-PET images, which also showed very high uptake of [ 18 F]FAA in spleen and visualization of tumors, and high uptake of [ 18 F]FXA in the heart. Conclusion: These results suggest that [ 18 F]FAA may be useful for tumor imaging, while [ 18 F]FXA may have potential as a heart imaging agent with PET

Research on simulated infrared image utility evaluation using deep representation

Science.gov (United States)

Zhang, Ruiheng; Mu, Chengpo; Yang, Yu; Xu, Lixin

2018-01-01

Infrared (IR) image simulation is an important data source for various target recognition systems. However, whether simulated IR images could be used as training data for classifiers depends on the features of fidelity and authenticity of simulated IR images. For evaluation of IR image features, a deep-representation-based algorithm is proposed. Being different from conventional methods, which usually adopt a priori knowledge or manually designed feature, the proposed method can extract essential features and quantitatively evaluate the utility of simulated IR images. First, for data preparation, we employ our IR image simulation system to generate large amounts of IR images. Then, we present the evaluation model of simulated IR image, for which an end-to-end IR feature extraction and target detection model based on deep convolutional neural network is designed. At last, the experiments illustrate that our proposed method outperforms other verification algorithms in evaluating simulated IR images. Cross-validation, variable proportion mixed data validation, and simulation process contrast experiments are carried out to evaluate the utility and objectivity of the images generated by our simulation system. The optimum mixing ratio between simulated and real data is 0.2≤γ≤0.3, which is an effective data augmentation method for real IR images.

Oncological applications of 18F-FDG PET imaging

International Nuclear Information System (INIS)

Li Lin

2000-01-01

Considering normal distribution of 18 F-FDG in human body, 18 F-FDG imaging using PET can be applied to brain tumors, colorectal cancer, lymphoma, melanoma, lung cancer and head and neck cancer. The author briefly focuses on application of 18 F-FDG PET imaging to breast cancer, pancreatic cancer, hepatocellular carcinoma, musculoskeletal neoplasms, endocrine neoplasms, genitourinary neoplasms, esophageal and gastric carcinomas

Improving face image extraction by using deep learning technique

Science.gov (United States)

Xue, Zhiyun; Antani, Sameer; Long, L. R.; Demner-Fushman, Dina; Thoma, George R.

2016-03-01

The National Library of Medicine (NLM) has made a collection of over a 1.2 million research articles containing 3.2 million figure images searchable using the Open-iSM multimodal (text+image) search engine. Many images are visible light photographs, some of which are images containing faces ("face images"). Some of these face images are acquired in unconstrained settings, while others are studio photos. To extract the face regions in the images, we first applied one of the most widely-used face detectors, a pre-trained Viola-Jones detector implemented in Matlab and OpenCV. The Viola-Jones detector was trained for unconstrained face image detection, but the results for the NLM database included many false positives, which resulted in a very low precision. To improve this performance, we applied a deep learning technique, which reduced the number of false positives and as a result, the detection precision was improved significantly. (For example, the classification accuracy for identifying whether the face regions output by this Viola- Jones detector are true positives or not in a test set is about 96%.) By combining these two techniques (Viola-Jones and deep learning) we were able to increase the system precision considerably, while avoiding the need to manually construct a large training set by manual delineation of the face regions.

Rapid Optical Detection and Classification of Microbes in Suspicious Powders

Science.gov (United States)

2018-06-01

Asher, “A New 224nm Hollow Cathode UV Laser Raman Spectrometer”, J. App. Spectroscopy, Vol. 55, No. 1, Jan 2001. [5] Storrie-Lombardi, M. C., W. F...Bhartia,R., E.C. Salas, W.F. Hug, R.D. Reid, A.L. Lane, K.J. Edwards, and K.J. Nealson, “Label-free bacterial imaging with deep UV laser induced...on natural surfaces using solar-blind deep UV excitation and detection. Detection is typically accomplished in less one second. The detection method

Two-Stage Approach to Image Classification by Deep Neural Networks

Science.gov (United States)

Ososkov, Gennady; Goncharov, Pavel

2018-02-01

The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying. An autoassociative neural network is used as a standalone autoencoder for prior extraction of the most informative features of the input data for neural networks to be compared further as classifiers. The main efforts to deal with deep learning networks are spent for a quite painstaking work of optimizing the structures of those networks and their components, as activation functions, weights, as well as the procedures of minimizing their loss function to improve their performances and speed up their learning time. It is also shown that the deep autoencoders develop the remarkable ability for denoising images after being specially trained. Convolutional Neural Networks are also used to solve a quite actual problem of protein genetics on the example of the durum wheat classification. Results of our comparative study demonstrate the undoubted advantage of the deep networks, as well as the denoising power of the autoencoders. In our work we use both GPU and cloud services to speed up the calculations.

VizieR Online Data Catalog: CANDELS multiwavelength catalog (Galametz+, 2013)

Science.gov (United States)

Galametz, A.; Grazian, A.; Fontana, A.; Ferguson, H. C.; Ashby, M. L. N.; Barro, G.; Castellano, M.; Dahlen, T.; Donley, J. L.; Faber, S. M.; Grogin, N.; Guo, Y.; Huang, K.-H.; Kocevski, D. D.; Koekemoer, A. M.; Lee, K.-S.; McGrath, E. J.; Peth, M.; Willner, S. P.; Almaini, O.; Cooper, M.; Cooray, A.; Conselice, C. J.; Dickinson, M.; Dunlop, J. S.; Fazio, G. G.; Foucaud, S.; Gardner, J. P.; Giavalisco, M.; Hathi, N. P.; Hartley, W. G.; Koo, D. C.; Lai, K.; de Mello, D. F.; McLure, R. J.; Lucas, R. A.; Paris, D.; Pentericci, L.; Santini, P.; Simpson, C.; Sommariva, V.; Targett, T.; Weiner, B. J.; Wuyts, S.; CANDELS Team

2013-06-01

The present multiwavelength catalog is based on public data in the CANDELS UDS field (J2000 position: 02:17:37.5-05:12:00) located within the original UDS field. It includes: * CANDELS HST/ACS (F606W, F814W) and HST/WFC3 (F125W, F160W); Grogin et al. 2011ApJS..197...35G, Koekemoer et al. 2011ApJS..197...36K. * CFHT U-band (UKIDSS; Almaini et al. in prep.), * SUBARU B, V, Rc, i', z' (SXDS; Furusawa et al. 2008, Cat. J/ApJS/176/1) * VLT/HAWK-I Y and Ks bands (HUGS; Fontana et al. in prep.) * UKIRT/WFCam J, H, K (UKIDSS DR8; Almaini et al. in prep.) * Spitzer/IRAC SEDS 3.6 and 4.5um (SEDS; Ashby et al. 2013ApJ...769...80A) * Spitzer/IRAC SpUDS 5.8, 8.0um (PI: J. Dunlop). The catalog is F160W-selected and contains 35932 sources over an area of 201.7 square arcmin and includes radio and X-ray detected sources and spectroscopic redshifts available for 210 sources. The full official CANDELS UDS catalog (which contains some extra columns including additional SExtractor parameters derived from the F160W image) can be found on the CANDELS website at: http://candels.ucolick.org/data_access/UDS.html (1 data file).

Improving the Astrometric Calibration of ACS/WFC for the Most Useful Filters

Science.gov (United States)

Anderson, Jay

2004-07-01

The distortion correction for the WFC, with which most ACS astrometry is done, is filter-dependent, and is not sufficiently accurate for the most useful filters to the community, F606W and F814W. We propose to derive improved corrections using 1 orbit for each filter. A by-product will be an astrometric standard field at the center of Omega Centauri.

The Stellar Populations Inside Expanding HI Shells in the Spiral Galaxy M33

Science.gov (United States)

Walterbos, Rene

1997-07-01

Because of its vigorous star formation activity, favorable inclination, and relative proximity, M33 is an ideal laboratory for the study of expanding HI shells in spiral galaxies. Theoretical models show that the energy deposited into the ISM by high mass stars in OB associations is capable of creating HI superbubbles. However, sparse observational evidence exists to test these models in detail. One essential ingredient of such a test is an improved census of stellar populations inside expanding HI shells. Using multi-color archival HST images of M33, we will {1} verify that association ages are consistent with dynamical ages of related shells and with ages from model predictions for bubbles of matching size and kinematics; {2} Constrain the IMF for each association by combining integrated ground-based HAlpha fluxes with the population age, present day mass function, and luminosity function derived from WFPC2 data; {3} Use this information to infer which fraction of the integrated stellar mechanical luminosity is transferred to a shell over its lifetime. Ground-based observations of associations inside expanding shells lack the UV-sensitivity and spatial resolution to adequately address these issues. Our sample of expanding neutral shells in M33 was selected using a new automated method for analysis of HI datacubes. From this robust catalog we have identified more than 30 HI supershells in M33 already imaged with WFPC2 in suitable broadband filters {F160BW, F170W, F336W, F439W, F555W, and F814W}.

Higgs boson production in deep inelastic lepton-nucleon scattering

International Nuclear Information System (INIS)

Abdullayev, S.Q.; Qocayev, M.Sh.; Saddi, F.A.

2016-01-01

In the framework of Standard Model the process of scalar Higgs boson production in deep inelastic lepton-nucleon scattering has been investigated: lN follows lHX, lN follows v l HX, v μ N follows v μ HX, v μ N follows μHX. The ZZ-fusion and WW-fusion mechanisms are the most important mechanisms for the production if Higgs bosons in lepton-nucleon deep inelastic scattering. It is shown that, the process l q follows lqH is defined by only four helicity amplitudes: F L L, F L R, F R L and F R R (here first and second indices show the helicity of lepton and quark), which describe the following reactions: l L q L follows l L q L H, l L q R follows l L q R H, l R q L follows l R q L H, l R q R follows l R q R H.The process v μ q follows v μ q H is defined by only two helicity amplitudes F L L and F L R, which describe reactions v μ q L follows v μ q L H and v μ q R follows v μ q q R H.The mechanism W W follows H is defined by one helicity amplitude, which describes the process l L q L follows v L q' L X or v μ q L follows μL q' L H.We have calculated the cross sections for the helicity processes and detailed numerical results are presented in the quark-patron model.

Quantum dots versus organic fluorophores in fluorescent deep-tissue imaging--merits and demerits.

Science.gov (United States)

Bakalova, Rumiana; Zhelev, Zhivko; Gadjeva, Veselina

2008-12-01

The use of fluorescence in deep-tissue imaging is rapidly expanding in last several years. The progress in fluorescent molecular probes and fluorescent imaging techniques gives an opportunity to detect single cells and even molecular targets in live organisms. The highly sensitive and high-speed fluorescent molecular sensors and detection devices allow the application of fluorescence in functional imaging. With the development of novel bright fluorophores based on nanotechnologies and 3D fluorescence scanners with high spatial and temporal resolution, the fluorescent imaging has a potential to become an alternative of the other non-invasive imaging techniques as magnetic resonance imaging, positron-emission tomography, X-ray, computing tomography. The fluorescent imaging has also a potential to give a real map of human anatomy and physiology. The current review outlines the advantages of fluorescent nanoparticles over conventional organic dyes in deep-tissue imaging in vivo and defines the major requirements to the "perfect fluorophore". The analysis proceeds from the basic principles of fluorescence and major characteristics of fluorophores, light-tissue interactions, and major limitations of fluorescent deep-tissue imaging. The article is addressed to a broad readership - from specialists in this field to university students.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

Science.gov (United States)

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust

Magnetic resonance imaging in deep pelvic endometriosis: iconographic essay

International Nuclear Information System (INIS)

Coutinho Junior, Antonio Carlos; Coutinho, Elisa Pompeu Dias; Lima, Claudio Marcio Amaral de Oliveira; Ribeiro, Erica Barreiros; Aidar, Marisa Nassar; Gasparetto, Emerson Leandro

2008-01-01

Endometriosis is characterized by the presence of normal endometrial tissue outside the uterine cavity. In patients with deep pelvic endometriosis, uterosacral ligaments, rectum, rectovaginal septum, vagina or bladder may be involved. Clinical manifestations may be variable, including pelvic pain, dysmenorrhea, dyspareunia, urinary symptoms and infertility. Complete surgical excision is the gold standard for treating this disease, and hence the importance of the preoperative work-up that usually is limited to an evaluation of sonographic and clinical data. Magnetic resonance imaging is of paramount importance in the diagnosis of endometriosis, considering its high accuracy in the identification of lesions intermingled with adhesions, and in the determination of peritoneal lesions extent. The present pictorial review describes the main magnetic resonance imaging findings in deep pelvic endometriosis. (author)

Magnetic resonance imaging in deep pelvic endometriosis: iconographic essay

Energy Technology Data Exchange (ETDEWEB)

Coutinho Junior, Antonio Carlos; Coutinho, Elisa Pompeu Dias; Lima, Claudio Marcio Amaral de Oliveira; Ribeiro, Erica Barreiros; Aidar, Marisa Nassar [Clinica de Diagnostico por Imagem (CDPI), Rio de Janeiro, RJ (Brazil); Clinica Multi-Imagem, Rio de Janeiro, RJ (Brazil); E-mail: cmaol@br.inter.net; Gasparetto, Emerson Leandro [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Dept. de Radiologia

2008-03-15

Endometriosis is characterized by the presence of normal endometrial tissue outside the uterine cavity. In patients with deep pelvic endometriosis, uterosacral ligaments, rectum, rectovaginal septum, vagina or bladder may be involved. Clinical manifestations may be variable, including pelvic pain, dysmenorrhea, dyspareunia, urinary symptoms and infertility. Complete surgical excision is the gold standard for treating this disease, and hence the importance of the preoperative work-up that usually is limited to an evaluation of sonographic and clinical data. Magnetic resonance imaging is of paramount importance in the diagnosis of endometriosis, considering its high accuracy in the identification of lesions intermingled with adhesions, and in the determination of peritoneal lesions extent. The present pictorial review describes the main magnetic resonance imaging findings in deep pelvic endometriosis. (author)

Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.

Science.gov (United States)

Nahid, Abdullah-Al; Mehrabi, Mohamad Ali; Kong, Yinan

2018-01-01

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F -Measure value is achieved on both the 40x and 100x datasets.

Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

Science.gov (United States)

Yu, Jun; Yang, Xiaokang; Gao, Fei; Tao, Dacheng

2017-12-01

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.

Radiologic image communication with fiberoptic media

International Nuclear Information System (INIS)

Huang, H.K.; Stewart, B.K.; Loloyan, M.; Tecotzky, R.

1990-01-01

Copper wires and coaxial cables are conventional media for transmitting radiologic images. The high impedance of these cables limits the speed of transmission, the bandwidth of the image, and the distance between nodes. This paper investigates characteristics of radiologic image communication with fiber optics as the medium. The model S L = F (B, D, M, C, W, TR) describes the signal loss S L of the image as a function (F) of the image bandwidth (B), the distance between two nodes (D), the mode of the fiber used (M), the connector type (C), the wavelength (W), and the characteristics of the optical transmitter and receiver pair (TR)

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

Science.gov (United States)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

[Advantages and Application Prospects of Deep Learning in Image Recognition and Bone Age Assessment].

Science.gov (United States)

Hu, T H; Wan, L; Liu, T A; Wang, M W; Chen, T; Wang, Y H

2017-12-01

Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Classification of time-series images using deep convolutional neural networks

Science.gov (United States)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Deep learning methods for CT image-domain metal artifact reduction

Science.gov (United States)

Gjesteby, Lars; Yang, Qingsong; Xi, Yan; Shan, Hongming; Claus, Bernhard; Jin, Yannan; De Man, Bruno; Wang, Ge

2017-09-01

Artifacts resulting from metal objects have been a persistent problem in CT images over the last four decades. A common approach to overcome their effects is to replace corrupt projection data with values synthesized from an interpolation scheme or by reprojection of a prior image. State-of-the-art correction methods, such as the interpolation- and normalization-based algorithm NMAR, often do not produce clinically satisfactory results. Residual image artifacts remain in challenging cases and even new artifacts can be introduced by the interpolation scheme. Metal artifacts continue to be a major impediment, particularly in radiation and proton therapy planning as well as orthopedic imaging. A new solution to the long-standing metal artifact reduction (MAR) problem is deep learning, which has been successfully applied to medical image processing and analysis tasks. In this study, we combine a convolutional neural network (CNN) with the state-of-the-art NMAR algorithm to reduce metal streaks in critical image regions. Training data was synthesized from CT simulation scans of a phantom derived from real patient images. The CNN is able to map metal-corrupted images to artifact-free monoenergetic images to achieve additional correction on top of NMAR for improved image quality. Our results indicate that deep learning is a novel tool to address CT reconstruction challenges, and may enable more accurate tumor volume estimation for radiation therapy planning.

Two-Stage Approach to Image Classification by Deep Neural Networks

Directory of Open Access Journals (Sweden)

Ososkov Gennady

2018-01-01

Full Text Available The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying. An autoassociative neural network is used as a standalone autoencoder for prior extraction of the most informative features of the input data for neural networks to be compared further as classifiers. The main efforts to deal with deep learning networks are spent for a quite painstaking work of optimizing the structures of those networks and their components, as activation functions, weights, as well as the procedures of minimizing their loss function to improve their performances and speed up their learning time. It is also shown that the deep autoencoders develop the remarkable ability for denoising images after being specially trained. Convolutional Neural Networks are also used to solve a quite actual problem of protein genetics on the example of the durum wheat classification. Results of our comparative study demonstrate the undoubted advantage of the deep networks, as well as the denoising power of the autoencoders. In our work we use both GPU and cloud services to speed up the calculations.

Large-Scale Image Analytics Using Deep Learning

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Ganguly, S.; Nemani, R. R.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Votava, P.

2014-12-01

High resolution land cover classification maps are needed to increase the accuracy of current Land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) land cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agricultural Imaging Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~100 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. In order to perform image analytics in such a granular system, it is mandatory to devise an intelligent archiving and query system for image retrieval, file structuring, metadata processing and filtering of all available image scenes. Using the Open NASA Earth Exchange (NEX) initiative, which is a partnership with Amazon Web Services (AWS), we have developed an end-to-end architecture for designing the database and the deep belief network (following the distbelief computing model) to solve a grand challenge of scaling this process across quarter million NAIP tiles that cover the entire Continental United States. The

Simultaneous functional imaging using fPET and fMRI

Energy Technology Data Exchange (ETDEWEB)

Villien, Marjorie [CERMEP (France)

2015-05-18

Brain mapping of task-associated changes in metabolism with PET has been accomplished by subtracting scans acquired during two distinct static states. We have demonstrated that PET can provide truly dynamic information on cerebral energy metabolism using constant infusion of FDG and multiple stimuli in a single experiment. We demonstrate here that the functional PET (fPET-FDG) method accomplished simultaneously with fMRI, can enable the first direct comparisons in time, space and magnitude of hemodynamics and oxygen and glucose consumption. The imaging studies were performed on a 3T Tim-Trio MR scanner modified to support an MR-compatible BrainPET insert. Ten healthy subjects were included. The total PET acquisition and infusion time was 90 minutes. We did 3 blocks of right hand fingers tapping for 10 minutes at 30, 50 and 70 minutes after the beginning of the PET acquisition. ASL and BOLD imaging were acquired simultaneously during the motor paradigm. Changes in glucose utilization are easily observed as changes in the TAC slope of the PET data (FDG utilization rate) and in the derivative signal during motor stimuli in the activated voxels. PET and MRI (ASL, and BOLD) activations are largely colocalized but with very different statistical significance and temporal dynamic, especially in the ipsilateral side of the stimuli. This study demonstrated that motor activation can be measured dynamically during a single FDG PET scan. The complementary nature of fPET-FDG to fMRI capitalizes on the emerging technology of hybrid MR-PET scanners. fPET-FDG, combined with quantitative fMRI methods, allow us to simultaneously measure dynamic changes in glucose utilization and hemodynamic, addressing vital questions about neurovascular coupling.

Simultaneous functional imaging using fPET and fMRI

International Nuclear Information System (INIS)

Villien, Marjorie

2015-01-01

Brain mapping of task-associated changes in metabolism with PET has been accomplished by subtracting scans acquired during two distinct static states. We have demonstrated that PET can provide truly dynamic information on cerebral energy metabolism using constant infusion of FDG and multiple stimuli in a single experiment. We demonstrate here that the functional PET (fPET-FDG) method accomplished simultaneously with fMRI, can enable the first direct comparisons in time, space and magnitude of hemodynamics and oxygen and glucose consumption. The imaging studies were performed on a 3T Tim-Trio MR scanner modified to support an MR-compatible BrainPET insert. Ten healthy subjects were included. The total PET acquisition and infusion time was 90 minutes. We did 3 blocks of right hand fingers tapping for 10 minutes at 30, 50 and 70 minutes after the beginning of the PET acquisition. ASL and BOLD imaging were acquired simultaneously during the motor paradigm. Changes in glucose utilization are easily observed as changes in the TAC slope of the PET data (FDG utilization rate) and in the derivative signal during motor stimuli in the activated voxels. PET and MRI (ASL, and BOLD) activations are largely colocalized but with very different statistical significance and temporal dynamic, especially in the ipsilateral side of the stimuli. This study demonstrated that motor activation can be measured dynamically during a single FDG PET scan. The complementary nature of fPET-FDG to fMRI capitalizes on the emerging technology of hybrid MR-PET scanners. fPET-FDG, combined with quantitative fMRI methods, allow us to simultaneously measure dynamic changes in glucose utilization and hemodynamic, addressing vital questions about neurovascular coupling.

The Hubble Space Telescope UV Legacy Survey of Galactic globular clusters - XIV. Multiple stellar populations within M 15 and their radial distribution

Science.gov (United States)

Nardiello, D.; Milone, A. P.; Piotto, G.; Anderson, J.; Bedin, L. R.; Bellini, A.; Cassisi, S.; Libralato, M.; Marino, A. F.

2018-06-01

In the context of the Hubble Space Telescope UV Survey of Galactic globular clusters (GCs), we derived high-precision, multi-band photometry to investigate the multiple stellar populations in the massive and metal-poor GC M 15. By creating for red-giant branch (RGB) stars of the cluster a `chromosome map', which is a pseudo two-colour diagram made with appropriate combination of F275W, F336W, F438W, and F814W magnitudes, we revealed colour spreads around two of the three already known stellar populations. These spreads cannot be produced by photometric errors alone and could hide the existence of (two) additional populations. This discovery increases the complexity of the multiple-population phenomenon in M 15. Our analysis shows that M 15 exhibits a faint sub-giant branch (SGB), which is also detected in colour-magnitude diagrams (CMDs) made with optical magnitudes only. This poorly populated SGB includes about 5 per cent of the total number of SGB stars and evolves into a red RGB in the mF336W versus mF336W - mF814W CMD, suggesting that M 15 belongs to the class of Type II GCs. We measured the relative number of stars in each population at various radial distances from the cluster centre, showing that all of these populations share the same radial distribution within statistic uncertainties. These new findings are discussed in the context of the formation and evolution scenarios of the multiple populations.

Fine-grained leukocyte classification with deep residual learning for microscopic images.

Science.gov (United States)

Qin, Feiwei; Gao, Nannan; Peng, Yong; Wu, Zizhao; Shen, Shuying; Grudtsin, Artur

2018-08-01

Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past development of machine learning techniques, for example, extracting distinctive features from raw microscopic images are difficult, the widely used SVM classifier only has relative few parameters to tune, these methods cannot efficiently handle fine-grained classification cases when the white blood cells have up to 40 categories. Based on deep learning theory, a systematic study is conducted on finer leukocyte classification in this paper. A deep residual neural network based leukocyte classifier is constructed at first, which can imitate the domain expert's cell recognition process, and extract salient features robustly and automatically. Then the deep neural network classifier's topology is adjusted according to the prior knowledge of white blood cell test. After that the microscopic image dataset with almost one hundred thousand labeled leukocytes belonging to 40 categories is built, and combined training strategies are adopted to make the designed classifier has good generalization ability. The proposed deep residual neural network based classifier was tested on microscopic image dataset with 40 leukocyte categories. It achieves top-1 accuracy of 77.80%, top-5 accuracy of 98.75% during the training procedure. The average accuracy on the test set is nearly 76.84%. This paper presents a fine-grained leukocyte classification method for microscopic images, based on deep residual learning theory and medical domain knowledge. Experimental results validate the feasibility and effectiveness of our approach. Extended experiments support that the fine-grained leukocyte classifier could be used in real medical applications, assist doctors in diagnosing diseases, reduce human power significantly. Copyright © 2018 Elsevier B.V. All rights reserved.

Toward a W4-F12 approach: Can explicitly correlated and orbital-based ab initio CCSD(T) limits be reconciled?

Energy Technology Data Exchange (ETDEWEB)

Sylvetsky, Nitai, E-mail: gershom@weizmann.ac.il; Martin, Jan M. L., E-mail: gershom@weizmann.ac.il [Department of Organic Chemistry, Weizmann Institute of Science, 76100 Rehovot (Israel); Peterson, Kirk A., E-mail: kipeters@wsu.edu [Department of Chemistry, Washington State University, Pullman, Washington 99164-4630 (United States); Karton, Amir, E-mail: amir.karton@uwa.edu.au [School of Chemistry and Biochemistry, The University of Western Australia, Perth, WA 6009 (Australia)

2016-06-07

In the context of high-accuracy computational thermochemistry, the valence coupled cluster with all singles and doubles (CCSD) correlation component of molecular atomization energies presents the most severe basis set convergence problem, followed by the (T) component. In the present paper, we make a detailed comparison, for an expanded version of the W4-11 thermochemistry benchmark, between, on the one hand, orbital-based CCSD/AV{5,6}Z + d and CCSD/ACV{5,6}Z extrapolation, and on the other hand CCSD-F12b calculations with cc-pVQZ-F12 and cc-pV5Z-F12 basis sets. This latter basis set, now available for H–He, B–Ne, and Al–Ar, is shown to be very close to the basis set limit. Apparent differences (which can reach 0.35 kcal/mol for systems like CCl{sub 4}) between orbital-based and CCSD-F12b basis set limits disappear if basis sets with additional radial flexibility, such as ACV{5,6}Z, are used for the orbital calculation. Counterpoise calculations reveal that, while total atomization energies with V5Z-F12 basis sets are nearly free of BSSE, orbital calculations have significant BSSE even with AV(6 + d)Z basis sets, leading to non-negligible differences between raw and counterpoise-corrected extrapolated limits. This latter problem is greatly reduced by switching to ACV{5,6}Z core-valence basis sets, or simply adding an additional zeta to just the valence orbitals. Previous reports that all-electron approaches like HEAT (high-accuracy extrapolated ab-initio thermochemistry) lead to different CCSD(T) limits than “valence limit + CV correction” approaches like Feller-Peterson-Dixon and Weizmann-4 (W4) theory can be rationalized in terms of the greater radial flexibility of core-valence basis sets. For (T) corrections, conventional CCSD(T)/AV{Q,5}Z + d calculations are found to be superior to scaled or extrapolated CCSD(T)-F12b calculations of similar cost. For a W4-F12 protocol, we recommend obtaining the Hartree-Fock and valence CCSD components from CCSD-F12b

Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging

Science.gov (United States)

Errico, Claudia; Pierre, Juliette; Pezet, Sophie; Desailly, Yann; Lenkei, Zsolt; Couture, Olivier; Tanter, Mickael

2015-11-01

Non-invasive imaging deep into organs at microscopic scales remains an open quest in biomedical imaging. Although optical microscopy is still limited to surface imaging owing to optical wave diffusion and fast decorrelation in tissue, revolutionary approaches such as fluorescence photo-activated localization microscopy led to a striking increase in resolution by more than an order of magnitude in the last decade. In contrast with optics, ultrasonic waves propagate deep into organs without losing their coherence and are much less affected by in vivo decorrelation processes. However, their resolution is impeded by the fundamental limits of diffraction, which impose a long-standing trade-off between resolution and penetration. This limits clinical and preclinical ultrasound imaging to a sub-millimetre scale. Here we demonstrate in vivo that ultrasound imaging at ultrafast frame rates (more than 500 frames per second) provides an analogue to optical localization microscopy by capturing the transient signal decorrelation of contrast agents—inert gas microbubbles. Ultrafast ultrasound localization microscopy allowed both non-invasive sub-wavelength structural imaging and haemodynamic quantification of rodent cerebral microvessels (less than ten micrometres in diameter) more than ten millimetres below the tissue surface, leading to transcranial whole-brain imaging within short acquisition times (tens of seconds). After intravenous injection, single echoes from individual microbubbles were detected through ultrafast imaging. Their localization, not limited by diffraction, was accumulated over 75,000 images, yielding 1,000,000 events per coronal plane and statistically independent pixels of ten micrometres in size. Precise temporal tracking of microbubble positions allowed us to extract accurately in-plane velocities of the blood flow with a large dynamic range (from one millimetre per second to several centimetres per second). These results pave the way for deep non

Making beautiful deep-sky images astrophotography with affordable equipment and software

CERN Document Server

Parker, Greg

2007-01-01

This book is based around the author's beautiful and sometimes awe-inspiring color images and mosaics of deep-sky objects. The book describes how similar "Hubble class" images can be created by amateur astronomers in their back garden.

Super-nonlinear fluorescence microscopy for high-contrast deep tissue imaging

Science.gov (United States)

Wei, Lu; Zhu, Xinxin; Chen, Zhixing; Min, Wei

2014-02-01

Two-photon excited fluorescence microscopy (TPFM) offers the highest penetration depth with subcellular resolution in light microscopy, due to its unique advantage of nonlinear excitation. However, a fundamental imaging-depth limit, accompanied by a vanishing signal-to-background contrast, still exists for TPFM when imaging deep into scattering samples. Formally, the focusing depth, at which the in-focus signal and the out-of-focus background are equal to each other, is defined as the fundamental imaging-depth limit. To go beyond this imaging-depth limit of TPFM, we report a new class of super-nonlinear fluorescence microscopy for high-contrast deep tissue imaging, including multiphoton activation and imaging (MPAI) harnessing novel photo-activatable fluorophores, stimulated emission reduced fluorescence (SERF) microscopy by adding a weak laser beam for stimulated emission, and two-photon induced focal saturation imaging with preferential depletion of ground-state fluorophores at focus. The resulting image contrasts all exhibit a higher-order (third- or fourth- order) nonlinear signal dependence on laser intensity than that in the standard TPFM. Both the physical principles and the imaging demonstrations will be provided for each super-nonlinear microscopy. In all these techniques, the created super-nonlinearity significantly enhances the imaging contrast and concurrently extends the imaging depth-limit of TPFM. Conceptually different from conventional multiphoton processes mediated by virtual states, our strategy constitutes a new class of fluorescence microscopy where high-order nonlinearity is mediated by real population transfer.

Confocal multispot microscope for fast and deep imaging in semicleared tissues

Science.gov (United States)

Adam, Marie-Pierre; Müllenbroich, Marie Caroline; Di Giovanna, Antonino Paolo; Alfieri, Domenico; Silvestri, Ludovico; Sacconi, Leonardo; Pavone, Francesco Saverio

2018-02-01

Although perfectly transparent specimens are imaged faster with light-sheet microscopy, less transparent samples are often imaged with two-photon microscopy leveraging its robustness to scattering; however, at the price of increased acquisition times. Clearing methods that are capable of rendering strongly scattering samples such as brain tissue perfectly transparent specimens are often complex, costly, and time intensive, even though for many applications a slightly lower level of tissue transparency is sufficient and easily achieved with simpler and faster methods. Here, we present a microscope type that has been geared toward the imaging of semicleared tissue by combining multispot two-photon excitation with rolling shutter wide-field detection to image deep and fast inside semicleared mouse brain. We present a theoretical and experimental evaluation of the point spread function and contrast as a function of shutter size. Finally, we demonstrate microscope performance in fixed brain slices by imaging dendritic spines up to 400-μm deep.

AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Maqlin Paramanandam

2016-11-01

Full Text Available The count of mitotic figures in Breast cancer histopathology slides is the most significant independent prognostic factor enabling determination of the proliferative activity of the tumor. In spite of the strict protocols followed, the mitotic counting activity suffers from subjectivity and considerable amount of observer variability despite being a laborious task. Interest in automated detection of mitotic figures has been rekindled with the advent of Whole Slide Scanners. Subsequently mitotic detection grand challenge contests have been held in recent years and several research methodologies developed by their participants. This paper proposes an efficient mitotic detection methodology for Hematoxylin and Eosin stained Breast cancer Histopathology Images using Gabor features and a Deep Belief Network- Deep Neural Network architecture (DBN-DNN. The proposed method has been evaluated on breast histopathology images from the publicly available dataset from MITOS contest held at the ICPR 2012 conference. It contains 226 mitoses annotated on 35 HPFs by several pathologists and 15 testing HPFs, yielding an F-measure of 0.74. In addition the said methodology was also tested on 3 slides from the MITOSIS- ATYPIA grand challenge held at the ICPR 2014 conference, an extension of MITOS containing 749 mitoses annotated on 1200 HPFs, by pathologists worldwide. This study has employed 3 slides (294 HPFs from the MITOS-ATYPIA training dataset in its evaluation and the results showed F-measures 0.65, 0.72and 0.74 for each slide. The proposed method is fast and computationally simple yet its accuracy and specificity is comparable to the best winning methods of the aforementioned grand challenges

DEEP HST /STIS VISIBLE-LIGHT IMAGING OF DEBRIS SYSTEMS AROUND SOLAR ANALOG HOSTS

Energy Technology Data Exchange (ETDEWEB)

Schneider, Glenn; Gaspar, Andras [Steward Observatory and the Department of Astronomy, The University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721 (United States); Grady, Carol A. [Eureka Scientific, 2452 Delmer, Suite 100, Oakland, CA 96002 (United States); Stark, Christopher C.; Kuchner, Marc J. [NASA/Goddard Space Flight Center, Exoplanets and Stellar Astrophysics Laboratory, Code 667, Greenbelt, MD 20771 (United States); Carson, Joseph [Department of Physics and Astronomy, College of Charleston, 66 George Street, Charleston, SC 29424 (United States); Debes, John H.; Hines, Dean C.; Perrin, Marshall [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Henning, Thomas [Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg (Germany); Jang-Condell, Hannah [Department of Physics and Astronomy, University of Wyoming, Laramie, WY 82071 (United States); Rodigas, Timothy J. [Department of Terrestrial Magnetism, Carnegie Institute of Washington, 5241 Branch Road, NW, Washington, DC 20015 (United States); Tamura, Motohide [The University of Tokyo, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo, 181-8588 (Japan); Wisniewski, John P., E-mail: gschneider@as.arizona.edu [H. L. Dodge Department of Physics and Astronomy, University of Oklahoma, 440 West Brooks Street, Norman, OK 73019 (United States)

2016-09-01

We present new Hubble Space Telescope observations of three a priori known starlight-scattering circumstellar debris systems (CDSs) viewed at intermediate inclinations around nearby close-solar analog stars: HD 207129, HD 202628, and HD 202917. Each of these CDSs possesses ring-like components that are more massive analogs of our solar system's Edgeworth–Kuiper Belt. These systems were chosen for follow-up observations to provide imaging with higher fidelity and better sensitivity for the sparse sample of solar-analog CDSs that range over two decades in systemic ages, with HD 202628 and HD 207129 (both ∼2.3 Gyr) currently the oldest CDSs imaged in visible or near-IR light. These deep (10–14 ks) observations, made with six-roll point-spread-function template visible-light coronagraphy using the Space Telescope Imaging Spectrograph, were designed to better reveal their angularly large debris rings of diffuse/low surface brightness, and for all targets probe their exo-ring environments for starlight-scattering materials that present observational challenges for current ground-based facilities and instruments. Contemporaneously also observing with a narrower occulter position, these observations additionally probe the CDS endo-ring environments that are seen to be relatively devoid of scatterers. We discuss the morphological, geometrical, and photometric properties of these CDSs also in the context of other CDSs hosted by FGK stars that we have previously imaged as a homogeneously observed ensemble. From this combined sample we report a general decay in quiescent-disk F {sub disk}/ F {sub star} optical brightness ∼ t {sup −0.8}, similar to what is seen at thermal IR wavelengths, and CDSs with a significant diversity in scattering phase asymmetries, and spatial distributions of their starlight-scattering grains.

StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

Directory of Open Access Journals (Sweden)

Pin Wu

2018-06-01

Full Text Available Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.

Preoperative staging of endometrial cancer using reduced field-of-view diffusion-weighted imaging: a preliminary study

Energy Technology Data Exchange (ETDEWEB)

Ota, Takashi; Hori, Masatoshi; Onishi, Hiromitsu; Sakane, Makoto; Tsuboyama, Takahiro; Tatsumi, Mitsuaki; Tomiyama, Noriyuki [Osaka University Graduate School of Medicine, Department of Diagnostic and Interventional Radiology, Suita, Osaka (Japan); Nakamoto, Atsushi; Narumi, Yoshifumi [Osaka Medical College, Department of Radiology, Osaka (Japan); Kimura, Tadashi [Osaka University Graduate School of Medicine, Department of Obstetrics and Gynaecology, Osaka (Japan)

2017-12-15

To compare the image quality and diagnostic performance of reduced field-of-view (rFOV) versus conventional full field-of-view (fFOV) diffusion-weighted (DW) imaging of endometrial cancer. Fifty women with endometrial cancer underwent preoperative rFOV and fFOV DW imaging. Two radiologists compared the image qualities of both techniques, and five radiologists assessed superficial and deep myometrial invasion using both techniques. The statistical analysis included the Wilcoxon signed-rank test and paired t-test for comparisons of image quality and mean diagnostic values. Distortion, tumour delineation, and overall image quality were significantly better with rFOV DW imaging, compared to fFOV DW imaging (P < 0.05); however, the former was inferior in noise (P < 0.05). Regarding superficial invasion, the mean accuracies of the techniques did not differ statistically (rFOV, 58.0% versus fFOV, 56.0%; P = 0.30). Regarding deep myometrial invasion, rFOV DW imaging yielded significantly better mean accuracy, specificity, and positive predictive values (88.4%, 97.8%, and 91.7%, respectively), compared with fFOV DW imaging (84.8%, 94.1%, and 77.4%, respectively; P = 0.009, 0.005, and 0.011, respectively). Compared with fFOV DW imaging, rFOV DW imaging yielded less distortion, improved image quality and, consequently, better diagnostic performance for deep myometrial invasion of endometrial cancer. (orig.)

Technical Note: Deep learning based MRAC using rapid ultra-short echo time imaging.

Science.gov (United States)

Jang, Hyungseok; Liu, Fang; Zhao, Gengyan; Bradshaw, Tyler; McMillan, Alan B

2018-05-15

In this study, we explore the feasibility of a novel framework for MR-based attenuation correction for PET/MR imaging based on deep learning via convolutional neural networks, which enables fully automated and robust estimation of a pseudo CT image based on ultrashort echo time (UTE), fat, and water images obtained by a rapid MR acquisition. MR images for MRAC are acquired using dual echo ramped hybrid encoding (dRHE), where both UTE and out-of-phase echo images are obtained within a short single acquisition (35 sec). Tissue labeling of air, soft tissue, and bone in the UTE image is accomplished via a deep learning network that was pre-trained with T1-weighted MR images. UTE images are used as input to the network, which was trained using labels derived from co-registered CT images. The tissue labels estimated by deep learning are refined by a conditional random field based correction. The soft tissue labels are further separated into fat and water components using the two-point Dixon method. The estimated bone, air, fat, and water images are then assigned appropriate Hounsfield units, resulting in a pseudo CT image for PET attenuation correction. To evaluate the proposed MRAC method, PET/MR imaging of the head was performed on 8 human subjects, where Dice similarity coefficients of the estimated tissue labels and relative PET errors were evaluated through comparison to a registered CT image. Dice coefficients for air (within the head), soft tissue, and bone labels were 0.76±0.03, 0.96±0.006, and 0.88±0.01. In PET quantification, the proposed MRAC method produced relative PET errors less than 1% within most brain regions. The proposed MRAC method utilizing deep learning with transfer learning and an efficient dRHE acquisition enables reliable PET quantification with accurate and rapid pseudo CT generation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

Science.gov (United States)

Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L

2018-04-01

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

The w-effect in interferometric imaging: from a fast sparse measurement operator to superresolution

Science.gov (United States)

Dabbech, A.; Wolz, L.; Pratley, L.; McEwen, J. D.; Wiaux, Y.

2017-11-01

Modern radio telescopes, such as the Square Kilometre Array, will probe the radio sky over large fields of view, which results in large w-modulations of the sky image. This effect complicates the relationship between the measured visibilities and the image under scrutiny. In algorithmic terms, it gives rise to massive memory and computational time requirements. Yet, it can be a blessing in terms of reconstruction quality of the sky image. In recent years, several works have shown that large w-modulations promote the spread spectrum effect. Within the compressive sensing framework, this effect increases the incoherence between the sensing basis and the sparsity basis of the signal to be recovered, leading to better estimation of the sky image. In this article, we revisit the w-projection approach using convex optimization in realistic settings, where the measurement operator couples the w-terms in Fourier and the de-gridding kernels. We provide sparse, thus fast, models of the Fourier part of the measurement operator through adaptive sparsification procedures. Consequently, memory requirements and computational cost are significantly alleviated at the expense of introducing errors on the radio interferometric data model. We present a first investigation of the impact of the sparse variants of the measurement operator on the image reconstruction quality. We finally analyse the interesting superresolution potential associated with the spread spectrum effect of the w-modulation, and showcase it through simulations. Our c++ code is available online on GitHub.

Deep Keck u-Band Imaging of the Hubble Ultra Deep Field: A Catalog of z ~ 3 Lyman Break Galaxies

Science.gov (United States)

Rafelski, Marc; Wolfe, Arthur M.; Cooke, Jeff; Chen, Hsiao-Wen; Armandroff, Taft E.; Wirth, Gregory D.

2009-10-01

We present a sample of 407 z ~ 3 Lyman break galaxies (LBGs) to a limiting isophotal u-band magnitude of 27.6 mag in the Hubble Ultra Deep Field. The LBGs are selected using a combination of photometric redshifts and the u-band drop-out technique enabled by the introduction of an extremely deep u-band image obtained with the Keck I telescope and the blue channel of the Low Resolution Imaging Spectrometer. The Keck u-band image, totaling 9 hr of integration time, has a 1σ depth of 30.7 mag arcsec-2, making it one of the most sensitive u-band images ever obtained. The u-band image also substantially improves the accuracy of photometric redshift measurements of ~50% of the z ~ 3 LBGs, significantly reducing the traditional degeneracy of colors between z ~ 3 and z ~ 0.2 galaxies. This sample provides the most sensitive, high-resolution multi-filter imaging of reliably identified z ~ 3 LBGs for morphological studies of galaxy formation and evolution and the star formation efficiency of gas at high redshift.

Detection of Thermal Erosion Gullies from High-Resolution Images Using Deep Learning

Science.gov (United States)

Huang, L.; Liu, L.; Jiang, L.; Zhang, T.; Sun, Y.

2017-12-01

Thermal erosion gullies, one type of thermokarst landforms, develop due to thawing of ice-rich permafrost. Mapping the location and extent of thermal erosion gullies can help understand the spatial distribution of thermokarst landforms and their temporal evolution. Remote sensing images provide an effective way for mapping thermokarst landforms, especially thermokarst lakes. However, thermal erosion gullies are challenging to map from remote sensing images due to their small sizes and significant variations in geometric/radiometric properties. It is feasible to manually identify these features, as a few previous studies have carried out. However manual methods are labor-intensive, therefore, cannot be used for a large study area. In this work, we conduct automatic mapping of thermal erosion gullies from high-resolution images by using Deep Learning. Our study area is located in Eboling Mountain (Qinghai, China). Within a 6 km2 peatland area underlain by ice-rich permafrost, at least 20 thermal erosional gullies are well developed. The image used is a 15-cm-resolution Digital Orthophoto Map (DOM) generated in July 2016. First, we extracted 14 gully patches and ten non-gully patches as training data. And we performed image augmentation. Next, we fine-tuned the pre-trained model of DeepLab, a deep-learning algorithm for semantic image segmentation based on Deep Convolutional Neural Networks. Then, we performed inference on the whole DOM and obtained intermediate results in forms of polygons for all identified gullies. At last, we removed misidentified polygons based on a few pre-set criteria on the size and shape of each polygon. Our final results include 42 polygons. Validated against field measurements using GPS, most of the gullies are detected correctly. There are 20 false detections due to the small number and low quality of training images. We also found three new gullies that missed in the field observations. This study shows that (1) despite a challenging

Fluorine-18 NaF PET imaging of child abuse

Energy Technology Data Exchange (ETDEWEB)

Drubach, Laura A. [Children' s Hospital Boston and Harvard Medical School, Department of Radiology, Division of Nuclear Medicine/PET, Boston, MA (United States); Sapp, Mark.V. [School of Osteopathic Medicine, Child Abuse Research Education and Services (CARES) Institute University of Medicine and Dentistry of New Jersey, New Jersey (United States); Laffin, Stephen [Children' s Hospital Boston, Department of Radiology, Division of Nuclear Medicine/PET, Boston, MA (United States); Kleinman, Paul K. [Children' s Hospital Boston and Harvard Medical School, Department of Radiology, Division of Musculoskeletal Imaging, Boston, MA (United States)

2008-07-15

We describe the use of {sup 18}F-NaF positron emission tomography (PET) whole-body imaging for the evaluation of skeletal trauma in a case of suspected child abuse. To our knowledge, 18F NaF PET has not been used in the past for the evaluation of child abuse. In our patient, this technique detected all sites of trauma shown by initial and follow-up skeletal surveys, including bilateral metaphyseal fractures of the proximal humeri. Fluorine-18 NaF PET has potential advantage over Tc-99m-labeled methylene diphosphonate (MDP) based upon superior image contrast and spatial resolution. (orig.)

Fluorine-18 NaF PET imaging of child abuse

International Nuclear Information System (INIS)

Drubach, Laura A.; Sapp, Mark V.; Laffin, Stephen; Kleinman, Paul K.

2008-01-01

We describe the use of 18 F-NaF positron emission tomography (PET) whole-body imaging for the evaluation of skeletal trauma in a case of suspected child abuse. To our knowledge, 18F NaF PET has not been used in the past for the evaluation of child abuse. In our patient, this technique detected all sites of trauma shown by initial and follow-up skeletal surveys, including bilateral metaphyseal fractures of the proximal humeri. Fluorine-18 NaF PET has potential advantage over Tc-99m-labeled methylene diphosphonate (MDP) based upon superior image contrast and spatial resolution. (orig.)

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.

Science.gov (United States)

Wang, Guan; Sun, Yu; Wang, Jianxin

2017-01-01

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.

Deep-tissue reporter-gene imaging with fluorescence and optoacoustic tomography: a performance overview.

Science.gov (United States)

Deliolanis, Nikolaos C; Ale, Angelique; Morscher, Stefan; Burton, Neal C; Schaefer, Karin; Radrich, Karin; Razansky, Daniel; Ntziachristos, Vasilis

2014-10-01

A primary enabling feature of near-infrared fluorescent proteins (FPs) and fluorescent probes is the ability to visualize deeper in tissues than in the visible. The purpose of this work is to find which is the optimal visualization method that can exploit the advantages of this novel class of FPs in full-scale pre-clinical molecular imaging studies. Nude mice were stereotactically implanted with near-infrared FP expressing glioma cells to from brain tumors. The feasibility and performance metrics of FPs were compared between planar epi-illumination and trans-illumination fluorescence imaging, as well as to hybrid Fluorescence Molecular Tomography (FMT) system combined with X-ray CT and Multispectral Optoacoustic (or Photoacoustic) Tomography (MSOT). It is shown that deep-seated glioma brain tumors are possible to visualize both with fluorescence and optoacoustic imaging. Fluorescence imaging is straightforward and has good sensitivity; however, it lacks resolution. FMT-XCT can provide an improved rough resolution of ∼1 mm in deep tissue, while MSOT achieves 0.1 mm resolution in deep tissue and has comparable sensitivity. We show imaging capacity that can shift the visualization paradigm in biological discovery. The results are relevant not only to reporter gene imaging, but stand as cross-platform comparison for all methods imaging near infrared fluorescent contrast agents.

HST/WFC3 Early Release Science In The GOODS-South Field: UV-dropout Galaxies At Z=2-3

Science.gov (United States)

Hathi, Nimish P.; Ryan, R. E., Jr.; Cohen, S. H.; Yan, H.; Windhorst, R. A.; McCarthy, P. J.; O'Connell, R. W.; Koekemoer, A. M.; SOC, WFC3

2010-01-01

We combine new high sensitivity of Ultraviolet (UV) imaging from the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST) with existing deep HST/ACS optical (F435W, F606W, F775W and F850LP) images from the GOODS program to identify UV-dropouts, which are galaxy candidates at z=2-3. These new HST/WFC3 observations were taken over 50 sq. arcminutes in the GOODS-South field as a part of the Early Release Science program. The uniqueness of these new UV data is that they are observed in 3 filters (F225W, F275W and F336W), which allows us to identify two different sets of UV-dropout samples. We apply (F275W-F336W) vs. (F336W-F435W) color selection criteria to identify F275W-dropout (z=2) galaxy candidates and (F336W-F435W) vs. (F435W-F606W) criteria to identify F336W-dropout (z=3) galaxy candidates. Multi-wavelength imaging and extensive spectroscopic follow-up observations in this field enable us to carefully access validity of our UV-dropout candidates. We estimate number counts and rest-frame UV Luminosity functions for galaxies at z=2-3, and these results are compared to other surveys at similar redshifts. This project is based on Early Release Science observations made by the WFC3 Scientific Oversight Committee. We are grateful to the Director of the Space Telescope Science Institute for awarding Director's Discretionary time for this program. Support for program #11359 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555.

Thoracic staging in lung cancer: prospective comparison of 18F-FDG PET/MR imaging and 18F-FDG PET/CT.

Science.gov (United States)

Heusch, Philipp; Buchbender, Christian; Köhler, Jens; Nensa, Felix; Gauler, Thomas; Gomez, Benedikt; Reis, Henning; Stamatis, Georgios; Kühl, Hilmar; Hartung, Verena; Heusner, Till A

2014-03-01

Therapeutic decisions in non-small cell lung cancer (NSCLC) patients depend on the tumor stage. PET/CT with (18)F-FDG is widely accepted as the diagnostic standard of care. The purpose of this study was to compare a dedicated pulmonary (18)F-FDG PET/MR imaging protocol with (18)F-FDG PET/CT for primary and locoregional lymph node staging in NSCLC patients using histopathology as the reference. Twenty-two patients (12 men, 10 women; mean age ± SD, 65.1 ± 9.1 y) with histopathologically confirmed NSCLC underwent (18)F-FDG PET/CT, followed by (18)F-FDG PET/MR imaging, including a dedicated pulmonary MR imaging protocol. T and N staging according to the seventh edition of the American Joint Committee on Cancer staging manual was performed by 2 readers in separate sessions for (18)F-FDG PET/CT and PET/MR imaging, respectively. Results from histopathology were used as the standard of reference. The mean and maximum standardized uptake value (SUV(mean) and SUV(max), respectively) and maximum diameter of the primary tumor was measured and compared in (18)F-FDG PET/CT and PET/MR imaging. PET/MR imaging and (18)F-FDG PET/CT agreed on T stages in 16 of 16 of patients (100%). All patients were correctly staged by (18)F-FDG PET/CT and PET/MR (100%), compared with histopathology. There was no statistically significant difference between (18)F-FDG PET/CT and (18)F-FDG PET/MR imaging for lymph node metastases detection (P = 0.48). For definition of thoracic N stages, PET/MR imaging and (18)F-FDG PET/CT were concordant in 20 of 22 patients (91%). PET/MR imaging determined the N stage correctly in 20 of 22 patients (91%). (18)F-FDG PET/CT determined the N stage correctly in 18 of 22 patients (82%). The mean differences for SUV(mean) and SUV(max) of NSCLC in (18)F-FDG PET/MR imaging and (18)F-FDG PET/CT were 0.21 and -5.06. These differences were not statistically significant (P > 0.05). The SUV(mean) and SUV(max) measurements derived from (18)F-FDG PET/CT and (18)F-FDG PET

Deep linear autoencoder and patch clustering-based unified one-dimensional coding of image and video

Science.gov (United States)

Li, Honggui

2017-09-01

This paper proposes a unified one-dimensional (1-D) coding framework of image and video, which depends on deep learning neural network and image patch clustering. First, an improved K-means clustering algorithm for image patches is employed to obtain the compact inputs of deep artificial neural network. Second, for the purpose of best reconstructing original image patches, deep linear autoencoder (DLA), a linear version of the classical deep nonlinear autoencoder, is introduced to achieve the 1-D representation of image blocks. Under the circumstances of 1-D representation, DLA is capable of attaining zero reconstruction error, which is impossible for the classical nonlinear dimensionality reduction methods. Third, a unified 1-D coding infrastructure for image, intraframe, interframe, multiview video, three-dimensional (3-D) video, and multiview 3-D video is built by incorporating different categories of videos into the inputs of patch clustering algorithm. Finally, it is shown in the results of simulation experiments that the proposed methods can simultaneously gain higher compression ratio and peak signal-to-noise ratio than those of the state-of-the-art methods in the situation of low bitrate transmission.

Uniform lateral etching of tungsten in deep trenches utilizing reaction-limited NF3 plasma process

Science.gov (United States)

Kofuji, Naoyuki; Mori, Masahito; Nishida, Toshiaki

2017-06-01

The reaction-limited etching of tungsten (W) with NF3 plasma was performed in an attempt to achieve the uniform lateral etching of W in a deep trench, a capability required by manufacturing processes for three-dimensional NAND flash memory. Reaction-limited etching was found to be possible at high pressures without ion irradiation. An almost constant etching rate that showed no dependence on NF3 pressure was obtained. The effect of varying the wafer temperature was also examined. A higher wafer temperature reduced the threshold pressure for reaction-limited etching and also increased the etching rate in the reaction-limited region. Therefore, the control of the wafer temperature is crucial to controlling the etching amount by this method. We found that the uniform lateral etching of W was possible even in a deep trench where the F radical concentration was low.

Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model

Science.gov (United States)

Ma, Ling; Lu, Guolan; Wang, Dongsheng; Wang, Xu; Chen, Zhuo Georgia; Muller, Susan; Chen, Amy; Fei, Baowei

2017-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.

Role of combined DWIBS/3D-CE-T1w whole-body MRI in tumor staging: Comparison with PET-CT

International Nuclear Information System (INIS)

Manenti, Guglielmo; Cicciò, Carmelo; Squillaci, Ettore; Strigari, Lidia; Calabria, Ferdinando; Danieli, Roberta

2012-01-01

Objectives: To assess the diagnostic performance of whole-body magnetic resonance imaging (WB-MRI) by diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) in malignant tumor detection and the potential diagnostic advantages in generating fused DWIBS/3D-contrast enhanced T1w (3D-CE-T1w) images. Methods: 45 cancer patients underwent 18F-FDG PET-CT and WB-MRI for staging purpose. Fused DWIBS/3D-CE T1w images were generated off-line. 3D-CE-T1w, DWIBS images alone and fused with 3D-CE T1w were compared by two readers groups for detection of primary diseases and local/distant metastases. Diagnostic performance between the three WB-MRI data sets was assessed using receiver operating characteristic (ROC) curve analysis. Imaging exams and histopathological results were used as standard of references. Results: Areas under the ROC curves of DWIBS vs. 3D-CE-T1w vs. both sequences in fused fashion were 0.97, 0.978, and 1.00, respectively. The diagnostic performance in tumor detection of fused DWIBS/3D-CE-T1w images were statistically superior to DWIBS (p < 0.001) and 3D-CE-T1w (p ≤ 0.002); while the difference between DWIBS and 3D-CE-T1w did not show statistical significance difference. Detection rates of malignancy did not differ between WB-MRI with DWIBS and 18F-FDG PET-CT. Conclusion: WB-MRI with DWIBS is to be considered as alternative tool to conventional whole-body methods for tumor staging and during follow-up in cancer patients.

INCITS W1.1 development update: appearance-based image quality standards for printers

Science.gov (United States)

Zeise, Eric K.; Rasmussen, D. René; Ng, Yee S.; Dalal, Edul; McCarthy, Ann; Williams, Don

2008-01-01

In September 2000, INCITS W1 (the U.S. representative of ISO/IEC JTC1/SC28, the standardization committee for office equipment) was chartered to develop an appearance-based image quality standard. (1),(2) The resulting W1.1 project is based on a proposal (3) that perceived image quality can be described by a small set of broad-based attributes. There are currently six ad hoc teams, each working towards the development of standards for evaluation of perceptual image quality of color printers for one or more of these image quality attributes. This paper summarizes the work in progress of the teams addressing the attributes of Macro-Uniformity, Colour Rendition, Gloss & Gloss Uniformity, Text & Line Quality and Effective Resolution.

THE DISTANCE TO THE MASSIVE GALACTIC CLUSTER WESTERLUND 2 FROM A SPECTROSCOPIC AND HST PHOTOMETRIC STUDY

International Nuclear Information System (INIS)

Vargas Álvarez, Carlos A.; Kobulnicky, Henry A.; Bradley, David R.; Kannappan, Sheila J.; Norris, Mark A.; Cool, Richard J.; Miller, Brendan P.

2013-01-01

We present a spectroscopic and photometric determination of the distance to the young Galactic open cluster Westerlund 2 using WFPC2 imaging from the Hubble Space Telescope (HST) and ground-based optical spectroscopy. HST imaging in the F336W, F439W, F555W, and F814W filters resolved many sources previously undetected in ground-based observations and yielded photometry for 1136 stars. We identified 15 new O-type stars, along with two probable binary systems, including MSP 188 (O3 + O5.5). We fit reddened spectral energy distributions based on the Padova isochrones to the photometric data to determine individual reddening parameters R V and A V for O-type stars in Wd2. We find average values (R V ) = 3.77 ± 0.09 and (A V ) = 6.51 ± 0.38 mag, which result in a smaller distance than most other spectroscopic and photometric studies. After a statistical distance correction accounting for close unresolved binaries (factor of 1.08), our spectroscopic and photometric data on 29 O-type stars yield that Westerlund 2 has a distance (d) = 4.16 ± 0.07 (random) +0.26 (systematic) kpc. The cluster's age remains poorly constrained, with an upper limit of 3 Myr. Finally, we report evidence of a faint mid-IR polycyclic aromatic hydrocarbon ring surrounding the well-known binary candidate MSP 18, which appears to lie at the center of a secondary stellar grouping within Westerlund 2.

THE DISTANCE TO THE MASSIVE GALACTIC CLUSTER WESTERLUND 2 FROM A SPECTROSCOPIC AND HST PHOTOMETRIC STUDY

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Vargas Alvarez, Carlos A.; Kobulnicky, Henry A. [Department of Physics and Astronomy, University of Wyoming, Dept. 3905, Laramie, WY 82071 (United States); Bradley, David R.; Kannappan, Sheila J.; Norris, Mark A. [Department of Physics and Astronomy, University of North Carolina, Chapel Hill, CB 3255, Phillips Hall, Chapel Hill, NC 27599-3255 (United States); Cool, Richard J. [Observatories of the Carnegie Institution of Washington, 813 Santa Barbara Street, Pasadena, CA 91101 (United States); Miller, Brendan P., E-mail: cvargasa@uwyo.edu, E-mail: chipk@uwyo.edu, E-mail: davidbradley512@gmail.com, E-mail: sheila@physics.unc.edu, E-mail: manorris@physics.unc.edu, E-mail: rcool@obs.carnegiescience.edu, E-mail: mbrendan@umich.edu [Department of Astronomy, University of Michigan, 745 Dennison Building, 500 Church St., Ann Arbor, MI 48109 (United States)

2013-05-15

We present a spectroscopic and photometric determination of the distance to the young Galactic open cluster Westerlund 2 using WFPC2 imaging from the Hubble Space Telescope (HST) and ground-based optical spectroscopy. HST imaging in the F336W, F439W, F555W, and F814W filters resolved many sources previously undetected in ground-based observations and yielded photometry for 1136 stars. We identified 15 new O-type stars, along with two probable binary systems, including MSP 188 (O3 + O5.5). We fit reddened spectral energy distributions based on the Padova isochrones to the photometric data to determine individual reddening parameters R{sub V} and A{sub V} for O-type stars in Wd2. We find average values (R{sub V} ) = 3.77 {+-} 0.09 and (A{sub V} ) = 6.51 {+-} 0.38 mag, which result in a smaller distance than most other spectroscopic and photometric studies. After a statistical distance correction accounting for close unresolved binaries (factor of 1.08), our spectroscopic and photometric data on 29 O-type stars yield that Westerlund 2 has a distance (d) = 4.16 {+-} 0.07 (random) +0.26 (systematic) kpc. The cluster's age remains poorly constrained, with an upper limit of 3 Myr. Finally, we report evidence of a faint mid-IR polycyclic aromatic hydrocarbon ring surrounding the well-known binary candidate MSP 18, which appears to lie at the center of a secondary stellar grouping within Westerlund 2.

Diffusion-weighted MR imaging in comparison to integrated [18F]-FDG PET/CT for N-staging in patients with lung cancer

International Nuclear Information System (INIS)

Pauls, Sandra; Schmidt, Stefan A.; Juchems, Markus S.; Klass, Oliver; Luster, Markus; Reske, Sven Norbert; Brambs, Hans-Juergen; Feuerlein, Sebastian

2012-01-01

Purpose The purpose of this study was to prospectively determine the diagnostic accuracy of diffusion-weighted imaging (DWI) using MRI in the staging of thoracic lymph nodes in patients with lung cancer, and to compare the performance to that of PET/CT. Patients and Method 20 consecutive patients (pts) with histologically proven lung cancer were included in this study. In all pts FDG-PET/CT was routinely performed to stage lung carcinoma. Additionally, MRI (1.5 T) was performed including native T1w, T1w post contrast medium, T2w, and DWI sequences. Regarding the N stage based on the results of the PET/CT there were 5 patients with N0, 3 patients with N1, 5 patients with N2 and 7 patients with N3. Image analysis was performed by two radiologists (R1 and R2), respectively. The reviewers had to chose between 1 (at least one lymph node within a station is malignant) or 0 (no lymph nodes suspicious for malignancy). First the T1 post contrast sequence was analyzed. In a second step the DWI sequence (b = 800) was analyzed. Both steps were performed in a blinded fashion. Results MR imaging with or without DWI only agreed with the results of the PET/CT regarding the N stage in 80% of the patients—15% were understaged and 5% overstaged. There was excellent interobserver agreement; the N-staging result only differed in 1 patient for DWI, resulting in correlation coefficients of 0.98 for DWI and 1.0 for MRI. Compared to PET-CT MRI overstaged one and understaged 4 patients, while DWI overstaged one and understaged 3 patients. This resulted in correlation coefficients of 0.814 (R1 and R2) for MRI and 0.815 (R1) and 0.804 (R2) for DWI. Regarding the ADC values there were no significant differences between ipsilateral hilar (1.03 mm 2 /s ± 0.13), subcarinal (0.96 mm 2 /s ± 0.24), ipsilateral mediastinal (1.0 mm 2 /s ± 0.18), contralateral mediastinal (0.93 mm 2 /s ± 0.23) and supraclavicular (0.9 mm 2 /s ± 0.23) lymph nodes. Conclusion Diffusion-weighted imaging does not

Imaging children suffering from lymphoma: an evaluation of different 18F-FDG PET/MRI protocols compared to whole-body DW-MRI.

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Kirchner, Julian; Deuschl, Cornelius; Schweiger, Bernd; Herrmann, Ken; Forsting, Michael; Buchbender, Christian; Antoch, Gerald; Umutlu, Lale

2017-09-01

The objectives of this study were to evaluate and compare the diagnostic potential of different PET/MRI reading protocols, entailing non-enhanced / contrast-enhanced and diffusion-weighted 18 F-FDG PET/MR imaging and whole-body diffusion-weighted MRI for lesion detection and determination of the tumor stage in pediatric lymphoma patients. A total of 28 18 F-FDG PET/MRI datasets were included for analysis of four different reading protocols: (1) PET/MRI utilizing sole unenhanced T2w and T1w imaging, (2) PET/MRI utilizing additional contrast enhanced sequences, (3) PET/MR imaging utilizing unenhanced, contrast enhanced and DW imaging or (4) WB-DW-MRI. Statistical analyses were performed on a per-patient and a per-lesion basis. Follow-up and prior examinations as well as histopathology served as reference standards. PET/MRI correctly identified all 17 examinations with active lymphoma disease, while WB-DW-MRI correctly identified 15/17 examinations. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy were 96%, 96.5%, 97%, 95%, and 96% for PET/MRI 1 ; 97%, 96.5%, 97%, 96.5%, and 97% for PET/MRI 2 ; 97%, 96.5%, 97%, 96.5%, and 97% for PET/MRI 3 and 77%, 96%, 96%, 78.5% and 86% for MRI-DWI. 18 F-FDG PET/MRI is superior to WB-DW-MRI in staging pediatric lymphoma patients. Neither application of contrast media nor DWI leads to a noticeable improvement of the diagnostic accuracy of PET/MRI. Thus, unenhanced PET/MRI may play a crucial role for the diagnostic work-up of pediatric lymphoma patients in the future.

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Directory of Open Access Journals (Sweden)

Guan Wang

2017-01-01

Full Text Available Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.

Tumor Metabolism and Perfusion in Head and Neck Squamous Cell Carcinoma: Pretreatment Multimodality Imaging With 1H Magnetic Resonance Spectroscopy, Dynamic Contrast-Enhanced MRI, and [18F]FDG-PET

International Nuclear Information System (INIS)

Jansen, Jacobus F.A.; Schöder, Heiko; Lee, Nancy Y.; Stambuk, Hilda E.; Wang Ya; Fury, Matthew G.; Patel, Senehal G.; Pfister, David G.; Shah, Jatin P.; Koutcher, Jason A.; Shukla-Dave, Amita

2012-01-01

Purpose: To correlate proton magnetic resonance spectroscopy ( 1 H-MRS), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and 18 F-labeled fluorodeoxyglucose positron emission tomography ([ 18 F]FDG PET) of nodal metastases in patients with head and neck squamous cell carcinoma (HNSCC) for assessment of tumor biology. Additionally, pretreatment multimodality imaging was evaluated for its efficacy in predicting short-term response to treatment. Methods and Materials: Metastatic neck nodes were imaged with 1 H-MRS, DCE-MRI, and [ 18 F]FDG PET in 16 patients with newly diagnosed HNSCC, before treatment. Short-term patient radiological response was evaluated at 3 to 4 months. Correlations among 1 H-MRS (choline concentration relative to water [Cho/W]), DCE-MRI (volume transfer constant [K trans ]; volume fraction of the extravascular extracellular space [v e ]; and redistribution rate constant [k ep ]), and [ 18 F]FDG PET (standard uptake value [SUV] and total lesion glycolysis [TLG]) were calculated using nonparametric Spearman rank correlation. To predict short-term responses, logistic regression analysis was performed. Results: A significant positive correlation was found between Cho/W and TLG (ρ = 0.599; p = 0.031). Cho/W correlated negatively with heterogeneity measures of standard deviation std(v e ) (ρ = −0.691; p = 0.004) and std(k ep ) (ρ = −0.704; p = 0.003). Maximum SUV (SUVmax) values correlated strongly with MRI tumor volume (ρ = 0.643; p = 0.007). Logistic regression indicated that std(K trans ) and SUVmean were significant predictors of short-term response (p 1 H-MRS, DCE-MRI, and [ 18 F]FDG PET is feasible in HNSCC patients with nodal metastases. Additionally, combined DCE-MRI and [ 18 F]FDG PET parameters were predictive of short-term response to treatment.

Efficient generation of image chips for training deep learning algorithms

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Han, Sanghui; Fafard, Alex; Kerekes, John; Gartley, Michael; Ientilucci, Emmett; Savakis, Andreas; Law, Charles; Parhan, Jason; Turek, Matt; Fieldhouse, Keith; Rovito, Todd

2017-05-01

Training deep convolutional networks for satellite or aerial image analysis often requires a large amount of training data. For a more robust algorithm, training data need to have variations not only in the background and target, but also radiometric variations in the image such as shadowing, illumination changes, atmospheric conditions, and imaging platforms with different collection geometry. Data augmentation is a commonly used approach to generating additional training data. However, this approach is often insufficient in accounting for real world changes in lighting, location or viewpoint outside of the collection geometry. Alternatively, image simulation can be an efficient way to augment training data that incorporates all these variations, such as changing backgrounds, that may be encountered in real data. The Digital Imaging and Remote Sensing Image Image Generation (DIRSIG) model is a tool that produces synthetic imagery using a suite of physics-based radiation propagation modules. DIRSIG can simulate images taken from different sensors with variation in collection geometry, spectral response, solar elevation and angle, atmospheric models, target, and background. Simulation of Urban Mobility (SUMO) is a multi-modal traffic simulation tool that explicitly models vehicles that move through a given road network. The output of the SUMO model was incorporated into DIRSIG to generate scenes with moving vehicles. The same approach was used when using helicopters as targets, but with slight modifications. Using the combination of DIRSIG and SUMO, we quickly generated many small images, with the target at the center with different backgrounds. The simulations generated images with vehicles and helicopters as targets, and corresponding images without targets. Using parallel computing, 120,000 training images were generated in about an hour. Some preliminary results show an improvement in the deep learning algorithm when real image training data are augmented with

Quantitative imaging of trace B in Si and SiO2 using ToF-SIMS

International Nuclear Information System (INIS)

Smentkowski, Vincent S.

2015-01-01

Changes in the oxidation state of an element can result in significant changes in the ionization efficiency and hence signal intensity during secondary ion mass spectrometry (SIMS) analysis; this is referred to as the SIMS matrix effect [Secondary Ion Mass Spectrometry: A Practical Handbook for Depth Profiling and Bulk Impurity Analysis, edited by R. G. Wilson, F. A. Stevie, and C. W. Magee (Wiley, New York, 1990)]. The SIMS matrix effect complicates quantitative analysis. Quantification of SIMS data requires the determination of relative sensitivity factors (RSFs), which can be used to convert the as measured intensity into concentration units [Secondary Ion Mass Spectrometry: A Practical Handbook for Depth Profiling and Bulk Impurity Analysis, edited by R. G. Wilson, F. A. Stevie, and C. W. Magee (Wiley, New York, 1990)]. In this manuscript, the authors report both: RSFs which were determined for quantification of B in Si and SiO 2 matrices using a dual beam time of flight secondary ion mass spectrometry (ToF-SIMS) instrument and the protocol they are using to provide quantitative ToF-SIMS images and line scan traces. The authors also compare RSF values that were determined using oxygen and Ar ion beams for erosion, discuss the problems that can be encountered when bulk calibration samples are used to determine RSFs, and remind the reader that errors in molecular details of the matrix (density, volume, etc.) that are used to convert from atoms/cm 3 to other concentration units will propagate into errors in the determined concentrations

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

Directory of Open Access Journals (Sweden)

Hua KL

2015-08-01

Full Text Available Kai-Lung Hua,1 Che-Hao Hsu,1 Shintami Chusnul Hidayati,1 Wen-Huang Cheng,2 Yu-Jen Chen3 1Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 2Research Center for Information Technology Innovation, Academia Sinica, 3Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan Abstract: Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. Keywords: nodule classification, deep learning, deep belief network, convolutional neural network

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

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Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

2017-10-01

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

Acceptance of the 2017 F.W. Clarke Award

Science.gov (United States)

McCubbin, Francis M.

2018-03-01

Thank you, Steelie, for that very kind and touching citation. Madam President and delegates of the 2017 Goldschmidt, I stand before you today both humbled and honored to receive the 2017F.W. Clarke Award from the Geochemical Society. It is quite intimidating to see the distinguished list of past recipients of this award. The accomplished careers of these individuals attest to the prestige of this great honor, and I consider myself fortunate to be listed among these individuals. Although I was elated by the news that I will receive this award, I also recognize that there are many other early career scientists that are equally deserving of such accolades. I consider it an honor to be part of such a strong community of early career geochemists, and I look forward to seeing the scientific accomplishments that will be achieved by our generation in the coming decades.

Clinical studies of 18F-FDG and 18F-FP-β-CIT PET imaging in hemi-Parkinson's disease

International Nuclear Information System (INIS)

Zhao Jun; Lin Xiangtong; Guan Yihui; Zuo Chuantao; Zhang Zhengwei; Wang Jian; Sun Bomin; Chen Zhengping

2003-01-01

Objective: To study the characteristics of 18 F-fluorodeoxyglucose (FDG) and 18 F-N-3-fluoro-propyl-2β-carbomethoxy-3β-(4-iodophenyl) nortropane ( 18 F-FP-β-CIT) PET imaging in patients with hemi-Parkinson's disease (hemi-PD) and to assess their value in early diagnosis. Methods: 34 cases of hemi-PD (Hoehn and Yahr stage I-II) and 16 normal control subjects were selected for this study. 16 patients were performed with 18 F-FDG PET imaging, 18 patients with 18 F-FP-β-CIF, while 6 patients of them both 18 F-FDG and 18 F-FP-β-CIT. 30 min after injection of 185-259 MBq 18 F-FDG, 3D brain scans were acquired. Region of interest (ROI) analysis and statistical parametric mapping (SPM) were applied. 18 F-FP-β-CIT PET imaging was carried out 2-3 h post injection, and (ROI-cerebellum)/cerebellum ratio was calculated. Results: In right hemi-PD, reductions in 18 F-FDG metabolism were observed in the left basal ganglia compared with control group, but with no significant difference (P>0.05). The results of SPM analysis showed that a significant reduction in FDG uptake in the left superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus and left middle temporal gyrus, whereas a significant increase in the bilateral precentral gyrus , superior parietal lobule, left middle occipital gyrus and left thalamus as compared with the control group. There was a significant reduction in 18 F-FP-β-CIT uptake in putamen, its reduction was found not only in the contralateral putamen, but also in the ipsilateral ones, and more pronounced in the contralateral posterior putamen. Conclusions: 18 F-FDG PET imaging is non-specific for the early diagnosis of PD. 18 F-FP-β-CIT PET imaging could find the changes of striatum dopamine transporter at early stage, therefore it was helpful for early diagnosis and differential diagnosis of PD. Combined with 18 F-FDG PET imaging, the changes of local cerebral glucose metabolism in PD could also be evaluated

17 CFR 8.14 - Admission or failure to deny charges.

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2010-04-01

... EXCHANGE PROCEDURES FOR DISCIPLINARY, SUMMARY, AND MEMBERSHIP DENIAL ACTIONS Disciplinary Procedure § 8.14... or fails to deny any of the charges the disciplinary committee may find that the rule violation... has been committed. If the exchange rules so provide, then: (1) The disciplinary committee shall...

[Automated Assessment for Bone Age of Left Wrist Joint in Uyghur Teenagers by Deep Learning].

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Hu, T H; Huo, Z; Liu, T A; Wang, F; Wan, L; Wang, M W; Chen, T; Wang, Y H

2018-02-01

To realize the automated bone age assessment by applying deep learning to digital radiography （DR） image recognition of left wrist joint in Uyghur teenagers, and explore its practical application value in forensic medicine bone age assessment. The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60％ of male and female DR images of left wrist joint were selected as net train set, and 10％ of samples were selected as validation set. As test set, the rest 30％ were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. The combination of bone age research on teenagers' left wrist joint and deep learning, which has high accuracy and good feasibility, can be the research basis of bone age automatic assessment system for the rest joints of body. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

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Srdjan Sladojevic

2016-01-01

Full Text Available The latest generation of convolutional neural networks (CNNs has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

DEEP KECK u-BAND IMAGING OF THE HUBBLE ULTRA DEEP FIELD: A CATALOG OF z ∼ 3 LYMAN BREAK GALAXIES

International Nuclear Information System (INIS)

Rafelski, Marc; Wolfe, Arthur M.; Cooke, Jeff; Chen, H.-W.; Armandroff, Taft E.; Wirth, Gregory D.

2009-01-01

We present a sample of 407 z ∼ 3 Lyman break galaxies (LBGs) to a limiting isophotal u-band magnitude of 27.6 mag in the Hubble Ultra Deep Field. The LBGs are selected using a combination of photometric redshifts and the u-band drop-out technique enabled by the introduction of an extremely deep u-band image obtained with the Keck I telescope and the blue channel of the Low Resolution Imaging Spectrometer. The Keck u-band image, totaling 9 hr of integration time, has a 1σ depth of 30.7 mag arcsec -2 , making it one of the most sensitive u-band images ever obtained. The u-band image also substantially improves the accuracy of photometric redshift measurements of ∼50% of the z ∼ 3 LBGs, significantly reducing the traditional degeneracy of colors between z ∼ 3 and z ∼ 0.2 galaxies. This sample provides the most sensitive, high-resolution multi-filter imaging of reliably identified z ∼ 3 LBGs for morphological studies of galaxy formation and evolution and the star formation efficiency of gas at high redshift.

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

Science.gov (United States)

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Methodological development of topographic correction in 2D/3D ToF-SIMS images using AFM images

Science.gov (United States)

Jung, Seokwon; Lee, Nodo; Choi, Myungshin; Lee, Jungmin; Cho, Eunkyunng; Joo, Minho

2018-02-01

Time-of-flight secondary-ion mass spectrometry (ToF-SIMS) is an emerging technique that provides chemical information directly from the surface of electronic materials, e.g. OLED and solar cell. It is very versatile and highly sensitive mass spectrometric technique that provides surface molecular information with their lateral distribution as a two-dimensional (2D) molecular image. Extending the usefulness of ToF-SIMS, a 3D molecular image can be generated by acquiring multiple 2D images in a stack. These imaging techniques by ToF-SIMS provide an insight into understanding the complex structures of unknown composition in electronic material. However, one drawback in ToF-SIMS is not able to represent topographical information in 2D and 3D mapping images. To overcome this technical limitation, topographic information by ex-situ technique such as atomic force microscopy (AFM) has been combined with chemical information from SIMS that provides both chemical and physical information in one image. The key to combine two different images obtained from ToF-SIMS and AFM techniques is to develop the image processing algorithm, which performs resize and alignment by comparing the specific pixel information of each image. In this work, we present methodological development of the semiautomatic alignment and the 3D structure interpolation system for the combination of 2D/3D images obtained by ToF-SIMS and AFM measurements, which allows providing useful analytical information in a single representation.

Atomic force microscopy deep trench and sidewall imaging with an optical fiber probe

Energy Technology Data Exchange (ETDEWEB)

Xie, Hui, E-mail: xiehui@hit.edu.cn; Hussain, Danish; Yang, Feng [The State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, 2 Yikuang, 150080 Harbin (China); Sun, Lining [The State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, 2 Yikuang, 150080 Harbin (China); Robotics and Microsystems Center, Soochow University, 215021 Suzhou (China)

2014-12-15

We report a method to measure critical dimensions of micro- and nanostructures using the atomic force microscope (AFM) with an optical fiber probe (OFP). This method is capable of scanning narrow and deep trenches due to the long and thin OFP tip, as well as imaging of steep sidewalls with unique profiling possibilities by laterally tilting the OFP without any modifications of the optical lever. A switch control scheme is developed to measure the sidewall angle by flexibly transferring feedback control between the Z- and Y-axis, for a serial scan of the horizontal surface (raster scan on XY-plane) and sidewall (raster scan on the YZ-plane), respectively. In experiments, a deep trench with tapered walls (243.5 μm deep) and a microhole (about 14.9 μm deep) have been imaged with the orthogonally aligned OFP, as well as a silicon sidewall (fabricated by deep reactive ion etching) has been characterized with the tilted OFP. Moreover, the sidewall angle of TGZ3 (AFM calibration grating) was accurately measured using the switchable scan method.

On-chip integrated functional near infra-red spectroscopy (fNIRS) photoreceiver for portable brain imaging

Science.gov (United States)

Kamrani, Ehsan

Optical brain imaging using functional near infra-red spectroscopy (fNIRS) offers a direct and noninvasive tool for monitoring of blood oxygenation. fNIRS is a noninvasive, safe, minimally intrusive, and high temporal-resolution technique for real-time and long-term brain imaging. It allows detecting both fast-neuronal and slow-hemodynamic signals. Besides the significant advantages of fNIRS systems, they still suffer from few drawbacks including low spatial-resolution, moderately high-level noise and high-sensitivity to movement. In order to overcome the limitations of currently available non-portable fNIRS systems, we have introduced a new low-power, miniaturized on-chip photodetector front-end intended for portable fNIRS systems. It includes silicon avalanche photodiode (SiAPD), Transimpedance amplifier (TIA), and Quench- Reset circuitry implemented using standard CMOS technologies to operate in both linear and Geiger modes. So it can be applied for both continuous-wave fNIRS (CW-fNIRS) and also single-photon counting applications. Several SiAPDs have been implemented in novel structures and shapes (Rectangular, Octagonal, Dual, Nested, Netted, Quadratic and Hexadecagonal) using different premature edge breakdown prevention techniques. The main characteristics of the SiAPDs are validated and the impact of each parameter and the device simulators (TCAD, COMSOL, etc.) have been studied based on the simulation and measurement results. Proposed techniques exhibit SiAPDs with high avalanche-gain (up to 119), low breakdown-voltage (around 12V) and high photon-detection efficiency (up to 72% in NIR region) in additional to a low dark-count rate (down to 30Hz at 1V excess bias voltage). Three new high gain-bandwidth product (GBW) and low-noise TIAs are introduced and implemented based on distributed-gain concept, logarithmic-amplification and automatic noise-rejection and have been applied in linear-mode of operation. The implemented TIAs offer a power

Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network.

Science.gov (United States)

Pal, Anabik; Garain, Utpal; Chandra, Aditi; Chatterjee, Raghunath; Senapati, Swapan

2018-06-01

Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images. Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers. An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches. The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease. Copyright © 2018 Elsevier B.V. All rights reserved.

Deep features for efficient multi-biometric recognition with face and ear images

Science.gov (United States)

Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

2017-07-01

Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

Deep erosions of the palmar aspect of the navicular bone diagnosed by standing magnetic resonance imaging.

Science.gov (United States)

Sherlock, C; Mair, T; Blunden, T

2008-11-01

Erosion of the palmar (flexor) aspect of the navicular bone is difficult to diagnose with conventional imaging techniques. To review the clinical, magnetic resonance (MR) and pathological features of deep erosions of the palmar aspect of the navicular bone. Cases of deep erosions of the palmar aspect of the navicular bone, diagnosed by standing low field MR imaging, were selected. Clinical details, results of diagnostic procedures, MR features and pathological findings were reviewed. Deep erosions of the palmar aspect of the navicular bone were diagnosed in 16 mature horses, 6 of which were bilaterally lame. Sudden onset of lameness was recorded in 63%. Radiography prior to MR imaging showed equivocal changes in 7 horses. The MR features consisted of focal areas of intermediate or high signal intensity on T1-, T2*- and T2-weighted images and STIR images affecting the dorsal aspect of the deep digital flexor tendon, the fibrocartilage of the palmar aspect, subchondral compact bone and medulla of the navicular bone. On follow-up, 7/16 horses (44%) had been subjected to euthanasia and only one was being worked at its previous level. Erosions of the palmar aspect of the navicular bone were confirmed post mortem in 2 horses. Histologically, the lesions were characterised by localised degeneration of fibrocartilage with underlying focal osteonecrosis and fibroplasia. The adjacent deep digital flexor tendon showed fibril formation and fibrocartilaginous metaplasia. Deep erosions of the palmar aspect of the navicular bone are more easily diagnosed by standing low field MR imaging than by conventional radiography. The lesions involve degeneration of the palmar fibrocartilage with underlying osteonecrosis and fibroplasia affecting the subchondral compact bone and medulla, and carry a poor prognosis for return to performance. Diagnosis of shallow erosive lesions of the palmar fibrocartilage may allow therapeutic intervention earlier in the disease process, thereby preventing

Imaging children suffering from lymphoma: an evaluation of different {sup 18}F-FDG PET/MRI protocols compared to whole-body DW-MRI

Energy Technology Data Exchange (ETDEWEB)

Kirchner, Julian; Buchbender, Christian; Antoch, Gerald [University Dusseldorf, Department of Diagnostic and Interventional Radiology, Medical Faculty, Dusseldorf (Germany); Deuschl, Cornelius; Schweiger, Bernd; Forsting, Michael; Umutlu, Lale [University Hospital Essen, University of Duisburg-Essen, Department of Diagnostic and Interventional Radiology and Neuroradiology, Essen (Germany); Herrmann, Ken [University Hospital Essen, University of Duisburg-Essen, Department of Nuclear Medicine, Essen (Germany)

2017-09-15

The objectives of this study were to evaluate and compare the diagnostic potential of different PET/MRI reading protocols, entailing non-enhanced / contrast-enhanced and diffusion-weighted {sup 18}F-FDG PET/MR imaging and whole-body diffusion-weighted MRI for lesion detection and determination of the tumor stage in pediatric lymphoma patients. A total of 28 {sup 18}F-FDG PET/MRI datasets were included for analysis of four different reading protocols: (1) PET/MRI utilizing sole unenhanced T2w and T1w imaging, (2) PET/MRI utilizing additional contrast enhanced sequences, (3) PET/MR imaging utilizing unenhanced, contrast enhanced and DW imaging or (4) WB-DW-MRI. Statistical analyses were performed on a per-patient and a per-lesion basis. Follow-up and prior examinations as well as histopathology served as reference standards. PET/MRI correctly identified all 17 examinations with active lymphoma disease, while WB-DW-MRI correctly identified 15/17 examinations. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy were 96%, 96.5%, 97%, 95%, and 96% for PET/MRI{sub 1}; 97%, 96.5%, 97%, 96.5%, and 97% for PET/MRI{sub 2}; 97%, 96.5%, 97%, 96.5%, and 97% for PET/MRI{sub 3} and 77%, 96%, 96%, 78.5% and 86% for MRI-DWI. {sup 18}F-FDG PET/MRI is superior to WB-DW-MRI in staging pediatric lymphoma patients. Neither application of contrast media nor DWI leads to a noticeable improvement of the diagnostic accuracy of PET/MRI. Thus, unenhanced PET/MRI may play a crucial role for the diagnostic work-up of pediatric lymphoma patients in the future. (orig.)

Measurement of Feynman-x spectra of photons and neutrons in the very forward direction in deep-inelastic scattering at HERA

Energy Technology Data Exchange (ETDEWEB)

Andreev, V.; Belousov, A.; Fomenko, A.; Gogitidze, N.; Lebedev, A.; Malinovski, E.; Rusakov, S.; Vazdik, Y. [Lebedev Physical Institute, Moscow (Russian Federation); Baghdasaryan, A.; Zohrabyan, H. [Yerevan Physics Institute, Yerevan (Armenia); Begzsuren, K.; Ravdandorj, T.; Tseepeldorj, B. [Institute of Physics and Technology of the Mongolian Academy of Sciences, Ulaanbaatar (Mongolia); Belov, P.; Brinkmann, M.; Britzger, D.; Campbell, A.J.; Dodonov, V.; Eckerlin, G.; Elsen, E.; Fleischer, M.; Gayler, J.; Ghazaryan, S.; Glazov, A.; Gouzevitch, M.; Haidt, D.; Kleinwort, C.; Krueger, K.; Levonian, S.; Lipka, K.; List, B.; List, J.; Lobodzinski, B.; Meyer, A.B.; Meyer, J.; Niebuhr, C.; Olsson, J.E.; Ozerov, D.; Pahl, P.; Petrukhin, A.; Pirumov, H.; Pitzl, D.; Placakyte, R.; Radescu, V.; Schmitt, S.; Sefkow, F.; Shushkevich, S.; South, D.; Steder, M.; Wuensch, E. [DESY, Hamburg (Germany); Boudry, V.; Specka, A. [LLR, Ecole Polytechnique, CNRS/IN2P3, Palaiseau (France); Brandt, G. [Oxford University, Department of Physics, Oxford (United Kingdom); Brisson, V.; Jacquet, M.; Pascaud, C.; Zhang, Z.; Zomer, F. [LAL, Universite Paris-Sud, CNRS/IN2P3, Orsay (France); Buniatyan, A.; Huber, F.; Sauter, M.; Schoening, A. [Universitaet Heidelberg, Physikalisches Institut, Heidelberg (Germany); Bylinkin, A.; Bystritskaya, L.; Fedotov, A.; Rostovtsev, A. [Institute for Theoretical and Experimental Physics, Moscow (Russian Federation); Cantun Avila, K.B.; Contreras, J.G. [CINVESTAV, Departamento de Fisica Aplicada, Merida, Yucatan (Mexico); Ceccopieri, F.; Favart, L.; Grebenyuk, A.; Hreus, T.; Janssen, X.; Roosen, R.; Mechelen, P. van [Brussels and Universiteit Antwerpen, Inter-University Institute for High Energies ULB-VUB, Antwerp (Belgium); Cerny, K.; Pokorny, B.; Polifka, R.; Salek, D.; Valkarova, A.; Zacek, J.; Zlebcik, R. [Charles University, Faculty of Mathematics and Physics, Prague (Czech Republic); Chekelian, V.; Grindhammer, G.; Kiesling, C. [Max-Planck-Institut fuer Physik, Munich (Germany); Dainton, J.B.; Gabathuler, E.; Greenshaw, T.; Klein, M.; Kostka, P.; Kretzschmar, J.; Laycock, P.; Maxfield, S.J.; Mehta, A.; Patel, G.D. [University of Liverpool, Department of Physics, Liverpool (United Kingdom); Daum, K.; Meyer, H. [Fachbereich C, Universitaet Wuppertal, Wuppertal (Germany); Diaconu, C.; Hoffmann, D.; Sauvan, E.; Vallee, C. [CPPM, Aix-Marseille Univ, CNRS/IN2P3, Marseille (France); Dobre, M.; Rotaru, M. [National Institute for Physics and Nuclear Engineering (NIPNE), Bucharest (Romania); Dossanov, A. [Universitaet Hamburg, Institut fuer Experimentalphysik, Hamburg (Germany); Max-Planck-Institut fuer Physik, Munich (Germany); Egli, S.; Horisberger, R. [Paul Scherrer Institut, Villigen (Switzerland); Feltesse, J.; Perez, E.; Schoeffel, L. [CEA, DSM/Irfu, CE-Saclay, Gif-sur-Yvette (France); Ferencei, J. [Slovak Academy of Sciences, Institute of Experimental Physics, Kosice (Slovakia); Goerlich, L.; Mikocki, S.; Nowak, G.; Sopicki, P.; Turnau, J. [Institute for Nuclear Physics, Cracow (Poland); Grab, C. [Institut fuer Teilchenphysik, ETH, Zurich (Switzerland); Henderson, R.C.W. [University of Lancaster, Department of Physics, Lancaster (United Kingdom); Herbst, M.; Schultz-Coulon, H.C. [Kirchhoff-Institut fuer Physik, Universitaet Heidelberg, Heidelberg (Germany); Hladky, J.; Reimer, P. [Academy of Sciences of the Czech Republic, Institute of Physics, Prague (Czech Republic); Jung, H. [Brussels and Universiteit Antwerpen, Inter-University Institute for High Energies ULB-VUB, Antwerp (Belgium); DESY, Hamburg (Germany); Kapichine, M.; Lytkin, L.; Morozov, A.; Spaskov, V. [Joint Institute for Nuclear Research, Dubna (Russian Federation); Kogler, R.; Nowak, K. [Universitaet Hamburg, Institut fuer Experimentalphysik, Hamburg (Germany); Landon, M.P.J.; Rizvi, E.; Traynor, D. [University of London, School of Physics and Astronomy, Queen Mary, London (GB); Lange, W.; Naumann, T. [DESY, Zeuthen (DE); Martyn, H.U. [I. Physikalisches Institut der RWTH, Aachen (DE); Mueller, K.; Robmann, P.; Straumann, U.; Truoel, P. [Physik-Institut der Universitaet Zuerich, Zurich (CH); Newman, P.R.; Thompson, P.D. [School of Physics and Astronomy, University of Birmingham, Birmingham (GB); Picuric, I.; Raicevic, N. [University of Montenegro, Faculty of Science, Podgorica (ME); Povh, B. [Max-Planck-Institut fuer Kernphysik, Heidelberg (DE); Sankey, D.P.C. [STFC, Rutherford Appleton Laboratory, Didcot, Oxfordshire (GB); Soloviev, Y. [DESY, Hamburg (DE); Lebedev Physical Institute, Moscow (RU); Stella, B. [Dipartimento di Fisica Universita di Roma Tre (IT); INFN Roma 3, Rome (IT); Sykora, T. [Brussels and Universiteit Antwerpen, Inter-University Institute for High Energies ULB-VUB, Antwerp (BE); Charles University, Faculty of Mathematics and Physics, Prague (CZ); Tsakov, I. [Institute for Nuclear Research and Nuclear Energy, Sofia (BG); Wegener, D. [Institut fuer Physik, TU Dortmund, Dortmund (DE); Collaboration: H1 Collaboration

2014-06-15

Measurements of normalised cross sections for the production of photons and neutrons at very small angles with respect to the proton beam direction in deep-inelastic ep scattering at HERA are presented as a function of the Feynman variable x{sub F} and of the centre-of-mass energy of the virtual photon-proton system W. The data are taken with the H1 detector in the years 2006 and 2007 and correspond to an integrated luminosity of 131 pb{sup -1}. The measurement is restricted to photons and neutrons in the pseudorapidity range η > 7.9 and covers the range of negative four momentum transfer squared at the positron vertex 6 < Q{sup 2} < 100 GeV{sup 2}, of inelasticity 0.05 < y < 0.6 and of 70 < W < 245 GeV. To test the Feynman scaling hypothesis the W dependence of the x{sub F} dependent cross sections is investigated. Predictions of deep-inelastic scattering models and of models for hadronic interactions of high energy cosmic rays are compared to the measured cross sections. (orig.)

Infection Imaging With 18F-FDS and First-in-Human Evaluation

International Nuclear Information System (INIS)

Yao, Shaobo; Xing, Haiqun; Zhu, Wenjia; Wu, Zhanhong; Zhang, Yingqiang; Ma, Yanru; Liu, Yimin; Huo, Li; Zhu, Zhaohui; Li, Zibo; Li, Fang

2016-01-01

Purpose: The noninvasive imaging of bacterial infections is critical in order to reduce mortality and morbidity caused by these diseases. The recently reported 18 F-FDS ( 18 F-2-fluorodeoxy sorbitol) as a PET (positron emission tomography) tracer can be used to image Enterobacteriaceae-specific infections and provides a potential alternative to this problem compared with other probes for imaging infections. In this study, automatic synthesis, validation of 18 F-FDS and a first-in-human study were performed and discussed. Methods: A multifunctional synthesis module was employed for the radiosynthesis of 18 F-FDG ( 18 F-2-fluorodeoxy glucose) and 18 F-FDS starting from 18 F ion using two-pot three-step fully automated reactions. The behavior of 18 F-FDS as an in vivo imaging probe for infections was evaluated in an Escherichia coli mouse infection model. The first detailed pharmacokinetic and biodistribution parameters were obtained from healthy human volunteers. Results: The uptake of 18 F-FDS in an E. coli mouse-myositis infection model was easily differentiated from other organs and normal muscle. Intensive lesion uptake declined after antibiotic treatment. In the pilot human study, no adverse effects due to 18 F-FDS were observed up to 24 h post-injection. The radiotracer was rapidly cleared from the circulation and excreted mainly through the urinary system. Conclusion: We conclude that 18 F-FDS PET holds great potential for appropriate and effective for the imaging of bacterial infections in vivo. These preliminary results indicate that further clinical studies are warranted.

Reflective all-sky thermal infrared cloud imager.

Science.gov (United States)

Redman, Brian J; Shaw, Joseph A; Nugent, Paul W; Clark, R Trevor; Piazzolla, Sabino

2018-04-30

A reflective all-sky imaging system has been built using a long-wave infrared microbolometer camera and a reflective metal sphere. This compact system was developed for measuring spatial and temporal patterns of clouds and their optical depth in support of applications including Earth-space optical communications. The camera is mounted to the side of the reflective sphere to leave the zenith sky unobstructed. The resulting geometric distortion is removed through an angular map derived from a combination of checkerboard-target imaging, geometric ray tracing, and sun-location-based alignment. A tape of high-emissivity material on the side of the reflector acts as a reference that is used to estimate and remove thermal emission from the metal sphere. Once a bias that is under continuing study was removed, sky radiance measurements from the all-sky imager in the 8-14 μm wavelength range agreed to within 0.91 W/(m 2 sr) of measurements from a previously calibrated, lens-based infrared cloud imager over its 110° field of view.

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

Science.gov (United States)

Hatipoglu, Nuh; Bilgin, Gokhan

2017-10-01

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

Science.gov (United States)

Zhou, Xiangrong; Yamada, Kazuma; Kojima, Takuya; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

2018-02-01

The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

Local Deep Hashing Matching of Aerial Images Based on Relative Distance and Absolute Distance Constraints

Directory of Open Access Journals (Sweden)

Suting Chen

2017-12-01

Full Text Available Aerial images have features of high resolution, complex background, and usually require large amounts of calculation, however, most algorithms used in matching of aerial images adopt the shallow hand-crafted features expressed as floating-point descriptors (e.g., SIFT (Scale-invariant Feature Transform, SURF (Speeded Up Robust Features, which may suffer from poor matching speed and are not well represented in the literature. Here, we propose a novel Local Deep Hashing Matching (LDHM method for matching of aerial images with large size and with lower complexity or fast matching speed. The basic idea of the proposed algorithm is to utilize the deep network model in the local area of the aerial images, and study the local features, as well as the hash function of the images. Firstly, according to the course overlap rate of aerial images, the algorithm extracts the local areas for matching to avoid the processing of redundant information. Secondly, a triplet network structure is proposed to mine the deep features of the patches of the local image, and the learned features are imported to the hash layer, thus obtaining the representation of a binary hash code. Thirdly, the constraints of the positive samples to the absolute distance are added on the basis of the triplet loss, a new objective function is constructed to optimize the parameters of the network and enhance the discriminating capabilities of image patch features. Finally, the obtained deep hash code of each image patch is used for the similarity comparison of the image patches in the Hamming space to complete the matching of aerial images. The proposed LDHM algorithm evaluates the UltraCam-D dataset and a set of actual aerial images, simulation result demonstrates that it may significantly outperform the state-of-the-art algorithm in terms of the efficiency and performance.

(18)F-FDG PET imaging of murine atherosclerosis

DEFF Research Database (Denmark)

Hag, Anne Mette Fisker; Pedersen, Sune Folke; Christoffersen, Christina

2012-01-01

To study whether (18)F-FDG can be used for in vivo imaging of atherogenesis by examining the correlation between (18)F-FDG uptake and gene expression of key molecular markers of atherosclerosis in apoE(-/-) mice....

The total dose effects on the 1/f noise of deep submicron CMOS transistors

International Nuclear Information System (INIS)

Hu Rongbin; Wang Yuxin; Lu Wu

2014-01-01

Using 0.18 μm CMOS transistors, the total dose effects on the 1/f noise of deep-submicron CMOS transistors are studied for the first time in mainland China. From the experimental results and the theoretic analysis, we realize that total dose radiation causes a lot of trapped positive charges in STI (shallow trench isolation) SiO 2 layers, which induces a current leakage passage, increasing the 1/f noise power of CMOS transistors. In addition, we design some radiation-hardness structures on the CMOS transistors and the experimental results show that, until the total dose achieves 750 krad, the 1/f noise power of the radiation-hardness CMOS transistors remains unchanged, which proves our conclusion. (semiconductor devices)

Texture analysis of T1-w and T2-w MR images allows a quantitative evaluation of radiation-induced changes of internal obturator muscles after radiotherapy for prostate cancer.

Science.gov (United States)

Scalco, Elisa; Rancati, Tiziana; Pirovano, Ileana; Mastropietro, Alfonso; Palorini, Federica; Cicchetti, Alessandro; Messina, Antonella; Avuzzi, Barbara; Valdagni, Riccardo; Rizzo, Giovanna

2018-04-01

To investigate the potential of texture analysis applied on T2-w and postcontrast T1-w images acquired before radiotherapy for prostate cancer (PCa) and 12 months after its completion in quantitatively characterizing local radiation effect on the muscular component of internal obturators, as organs potentially involved in urinary toxicity. T2-w and postcontrast T1-w MR images were acquired at 1.5 T before treatment (MRI1) and at 12 months of follow-up (MRI2) in 13 patients treated with radiotherapy for PCa. Right and left internal obturator muscle contours were manually delineated upon MRI1 and then automatically propagated on MRI2 by an elastic registration method. Planning CT images were coregistered to both MRIs and dose maps were deformed accordingly. A high-dose region receiving >55 Gy and a low-dose region receiving evaluated. A signal increase was highlighted in both T2-w and T1-w images in the portion of the obturators near the prostate, i.e., in the region receiving medium-high doses. A change in the spatial organization was identified, as an increase in homogeneity and a decrease in contrast and complexity, compatible with an inflammatory status. In particular, the region receiving medium-high doses presented more significant or, at least, stronger differences. Texture analysis applied on T1-w and T2-w MR images has demonstrated its ability in quantitative evaluating radiation-induced changes in obturator muscles after PCa radiotherapy. © 2018 American Association of Physicists in Medicine.

STAR FORMATION ACROSS THE W3 COMPLEX

Energy Technology Data Exchange (ETDEWEB)

Román-Zúñiga, Carlos G.; Ybarra, Jason E.; Tapia, Mauricio [Instituto de Astronomía, Universidad Nacional Autónoma de México, Unidad Académica en Ensenada, Km 103 Carr. Tijuana–Ensenada, Ensenada 22860 (Mexico); Megías, Guillermo D. [Facultad de Física. Universidad de Sevilla. Dpto. Física Atómica, Molecular y Nuclear, Sevilla, E-41080 (Spain); Lada, Elizabeth A. [Astronomy Department, University of Florida, 211 Bryant Space Sciences Center, FL 32611 (United States); Alves, Joáo F. [Institute of Astronomy, University of Vienna, Türkenschanzstr. 17, A-1180 Vienna (Austria)

2015-09-15

We present a multi-wavelength analysis of the history of star formation in the W3 complex. Using deep, near-infrared ground-based images combined with images obtained with Spitzer and Chandra observatories, we identified and classified young embedded sources. We identified the principal clusters in the complex and determined their structure and extension. We constructed extinction-limited samples for five principal clusters and constructed K-band luminosity functions that we compare with those of artificial clusters with varying ages. This analysis provided mean ages and possible age spreads for the clusters. We found that IC 1795, the centermost cluster of the complex, still hosts a large fraction of young sources with circumstellar disks. This indicates that star formation was active in IC 1795 as recently as 2 Myr ago, simultaneous to the star-forming activity in the flanking embedded clusters, W3-Main and W3(OH). A comparison with carbon monoxide emission maps indicates strong velocity gradients in the gas clumps hosting W3-Main and W3(OH) and shows small receding clumps of gas at IC 1795, suggestive of rapid gas removal (faster than the T Tauri timescale) in the cluster-forming regions. We discuss one possible scenario for the progression of cluster formation in the W3 complex. We propose that early processes of gas collapse in the main structure of the complex could have defined the progression of cluster formation across the complex with relatively small age differences from one group to another. However, triggering effects could act as catalysts for enhanced efficiency of formation at a local level, in agreement with previous studies.

Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image

Directory of Open Access Journals (Sweden)

YiNan Zhang

2017-01-01

Full Text Available Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other types of images can be added into training dataset selectively. In empirical experiments, results of eight distinctive medical images show improvement of image quality and time reduction. Further, the proposed method also produces slightly sharper edges than other deep learning approaches in less time and it is projected that the hybrid architecture of prefixed template layer and unfixed hidden layers has potentials in other applications.

Deep learning and three-compartment breast imaging in breast cancer diagnosis

Science.gov (United States)

Drukker, Karen; Huynh, Benjamin Q.; Giger, Maryellen L.; Malkov, Serghei; Avila, Jesus I.; Fan, Bo; Joe, Bonnie; Kerlikowske, Karla; Drukteinis, Jennifer S.; Kazemi, Leila; Pereira, Malesa M.; Shepherd, John

2017-03-01

We investigated whether deep learning has potential to aid in the diagnosis of breast cancer when applied to mammograms and biologic tissue composition images derived from three-compartment (3CB) imaging. The dataset contained diagnostic mammograms and 3CB images (water, lipid, and protein content) of biopsy-sampled BIRADS 4 and 5 lesions in 195 patients. In 58 patients, the lesion manifested as a mass (13 malignant vs. 45 benign), in 87 as microcalcifications (19 vs. 68), and in 56 as (focal) asymmetry or architectural distortion (11 vs. 45). Six patients had both a mass and calcifications. For each mammogram and corresponding 3CB images, a 128x128 region of interest containing the lesion was selected by an expert radiologist and used directly as input to a deep learning method pretrained on a very large independent set of non-medical images. We used a nested leave-one-out-by-case (patient) model selection and classification protocol. The area under the ROC curve (AUC) for the task of distinguishing between benign and malignant lesions was used as performance metric. For the cases with mammographic masses, the AUC increased from 0.83 (mammograms alone) to 0.89 (mammograms+3CB, p=.162). For the microcalcification and asymmetry/architectural distortion cases the AUC increased from 0.84 to 0.91 (p=.116) and from 0.61 to 0.87 (p=.006), respectively. Our results indicate great potential for the application of deep learning methods in the diagnosis of breast cancer and additional knowledge of the biologic tissue composition appeared to improve performance, especially for lesions mammographically manifesting as asymmetries or architectural distortions.

Big-deep-smart data in imaging for guiding materials design

Science.gov (United States)

Kalinin, Sergei V.; Sumpter, Bobby G.; Archibald, Richard K.

2015-10-01

Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the design and realization of advanced functional materials. Here we discuss new opportunities in materials design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.

Measurement of the Polarization of W Bosons with Large Transverse Momenta in W+Jets Events at the LHC

CERN Document Server

Chatrchyan, Serguei; Sirunyan, Albert M; Tumasyan, Armen; Adam, Wolfgang; Bergauer, Thomas; Dragicevic, Marko; Erö, Janos; Fabjan, Christian; Friedl, Markus; Fruehwirth, Rudolf; Ghete, Vasile Mihai; Hammer, Josef; Haensel, Stephan; Hoch, Michael; Hörmann, Natascha; Hrubec, Josef; Jeitler, Manfred; Kiesenhofer, Wolfgang; Krammer, Manfred; Liko, Dietrich; Mikulec, Ivan; Pernicka, Manfred; Rohringer, Herbert; Schöfbeck, Robert; Strauss, Josef; Taurok, Anton; Teischinger, Florian; Wagner, Philipp; Waltenberger, Wolfgang; Walzel, Gerhard; Widl, Edmund; Wulz, Claudia-Elisabeth; Mossolov, Vladimir; Shumeiko, Nikolai; Suarez Gonzalez, Juan; Benucci, Leonardo; De Wolf, Eddi A; Janssen, Xavier; Maes, Joris; Maes, Thomas; Mucibello, Luca; Ochesanu, Silvia; Roland, Benoit; Rougny, Romain; Selvaggi, Michele; Van Haevermaet, Hans; Van Mechelen, Pierre; Van Remortel, Nick; Blekman, Freya; Blyweert, Stijn; D'Hondt, Jorgen; Devroede, Olivier; Gonzalez Suarez, Rebeca; Kalogeropoulos, Alexis; Maes, Michael; Van Doninck, Walter; Van Mulders, Petra; Van Onsem, Gerrit Patrick; Villella, Ilaria; Charaf, Otman; Clerbaux, Barbara; De Lentdecker, Gilles; Dero, Vincent; Gay, Arnaud; Hammad, Gregory Habib; Hreus, Tomas; Marage, Pierre Edouard; Thomas, Laurent; Vander Velde, Catherine; Vanlaer, Pascal; Adler, Volker; Cimmino, Anna; Costantini, Silvia; Grunewald, Martin; Klein, Benjamin; Lellouch, Jérémie; Marinov, Andrey; Mccartin, Joseph; Ryckbosch, Dirk; Thyssen, Filip; Tytgat, Michael; Vanelderen, Lukas; Verwilligen, Piet; Walsh, Sinead; Zaganidis, Nicolas; Basegmez, Suzan; Bruno, Giacomo; Caudron, Julien; Ceard, Ludivine; Cortina Gil, Eduardo; De Favereau De Jeneret, Jerome; Delaere, Christophe; Favart, Denis; Giammanco, Andrea; Grégoire, Ghislain; Hollar, Jonathan; Lemaitre, Vincent; Liao, Junhui; Militaru, Otilia; Ovyn, Severine; Pagano, Davide; Pin, Arnaud; Piotrzkowski, Krzysztof; Schul, Nicolas; Beliy, Nikita; Caebergs, Thierry; Daubie, Evelyne; Alves, Gilvan; De Jesus Damiao, Dilson; Pol, Maria Elena; Henrique Gomes E Souza, Moacyr; Carvalho, Wagner; Melo Da Costa, Eliza; De Oliveira Martins, Carley; Fonseca De Souza, Sandro; Mundim, Luiz; Nogima, Helio; Oguri, Vitor; Prado Da Silva, Wanda Lucia; Santoro, Alberto; Silva Do Amaral, Sheila Mara; Sznajder, Andre; Torres Da Silva De Araujo, Felipe; De Almeida Dias, Flavia; Tomei, Thiago; De Moraes Gregores, Eduardo; Lagana, Caio; Da Cunha Marinho, Franciole; Mercadante, Pedro G; Novaes, Sergio F; Padula, Sandra; Darmenov, Nikolay; Dimitrov, Lubomir; Genchev, Vladimir; Iaydjiev, Plamen; Piperov, Stefan; Rodozov, Mircho; Stoykova, Stefka; Sultanov, Georgi; Tcholakov, Vanio; Trayanov, Rumen; Vankov, Ivan; Dimitrov, Anton; Hadjiiska, Roumyana; Karadzhinova, Aneliya; Kozhuharov, Venelin; Litov, Leander; Mateev, Matey; Pavlov, Borislav; Petkov, Peicho; Bian, Jian-Guo; Chen, Guo-Ming; Chen, He-Sheng; Jiang, Chun-Hua; Liang, Dong; Liang, Song; Meng, Xiangwei; Tao, Junquan; Wang, Jian; Wang, Jian; Wang, Xianyou; Wang, Zheng; Xiao, Hong; Xu, Ming; Zang, Jingjing; Zhang, Zhen; Ban, Yong; Guo, Shuang; Guo, Yifei; Li, Wenbo; Mao, Yajun; Qian, Si-Jin; Teng, Haiyun; Zhang, Linlin; Zhu, Bo; Zou, Wei; Cabrera, Andrés; Gomez Moreno, Bernardo; Ocampo Rios, Alberto Andres; Osorio Oliveros, Andres Felipe; Sanabria, Juan Carlos; Godinovic, Nikola; Lelas, Damir; Lelas, Karlo; Plestina, Roko; Polic, Dunja; Puljak, Ivica; Antunovic, Zeljko; Dzelalija, Mile; Brigljevic, Vuko; Duric, Senka; Kadija, Kreso; Morovic, Srecko; Attikis, Alexandros; Galanti, Mario; Mousa, Jehad; Nicolaou, Charalambos; Ptochos, Fotios; Razis, Panos A; Finger, Miroslav; Finger Jr, Michael; Assran, Yasser; Khalil, Shaaban; Mahmoud, Mohammed; Hektor, Andi; Kadastik, Mario; Müntel, Mait; Raidal, Martti; Rebane, Liis; Azzolini, Virginia; Eerola, Paula; Fedi, Giacomo; Czellar, Sandor; Härkönen, Jaakko; Heikkinen, Mika Aatos; Karimäki, Veikko; Kinnunen, Ritva; Kortelainen, Matti J; Lampén, Tapio; Lassila-Perini, Kati; Lehti, Sami; Lindén, Tomas; Luukka, Panja-Riina; Mäenpää, Teppo; Tuominen, Eija; Tuominiemi, Jorma; Tuovinen, Esa; Ungaro, Donatella; Wendland, Lauri; Banzuzi, Kukka; Korpela, Arja; Tuuva, Tuure; Sillou, Daniel; Besancon, Marc; Choudhury, Somnath; Dejardin, Marc; Denegri, Daniel; Fabbro, Bernard; Faure, Jean-Louis; Ferri, Federico; Ganjour, Serguei; Gentit, François-Xavier; Givernaud, Alain; Gras, Philippe; Hamel de Monchenault, Gautier; Jarry, Patrick; Locci, Elizabeth; Malcles, Julie; Marionneau, Matthieu; Millischer, Laurent; Rander, John; Rosowsky, André; Shreyber, Irina; Titov, Maksym; Verrecchia, Patrice; Baffioni, Stephanie; Beaudette, Florian; Benhabib, Lamia; Bianchini, Lorenzo; Bluj, Michal; Broutin, Clementine; Busson, Philippe; Charlot, Claude; Dahms, Torsten; Dobrzynski, Ludwik; Elgammal, Sherif; Granier de Cassagnac, Raphael; Haguenauer, Maurice; Miné, Philippe; Mironov, Camelia; Ochando, Christophe; Paganini, Pascal; Sabes, David; Salerno, Roberto; Sirois, Yves; Thiebaux, Christophe; Wyslouch, Bolek; Zabi, Alexandre; Agram, Jean-Laurent; Andrea, Jeremy; Bloch, Daniel; Bodin, David; Brom, Jean-Marie; Cardaci, Marco; Chabert, Eric Christian; Collard, Caroline; Conte, Eric; Drouhin, Frédéric; Ferro, Cristina; Fontaine, Jean-Charles; Gelé, Denis; Goerlach, Ulrich; Greder, Sebastien; Juillot, Pierre; Karim, Mehdi; Le Bihan, Anne-Catherine; Mikami, Yoshinari; Van Hove, Pierre; Fassi, Farida; Mercier, Damien; Baty, Clement; Beauceron, Stephanie; Beaupere, Nicolas; Bedjidian, Marc; Bondu, Olivier; Boudoul, Gaelle; Boumediene, Djamel; Brun, Hugues; Chasserat, Julien; Chierici, Roberto; Contardo, Didier; Depasse, Pierre; El Mamouni, Houmani; Fay, Jean; Gascon, Susan; Ille, Bernard; Kurca, Tibor; Le Grand, Thomas; Lethuillier, Morgan; Mirabito, Laurent; Perries, Stephane; Sordini, Viola; Tosi, Silvano; Tschudi, Yohann; Verdier, Patrice; Lomidze, David; Anagnostou, Georgios; Edelhoff, Matthias; Feld, Lutz; Heracleous, Natalie; Hindrichs, Otto; Jussen, Ruediger; Klein, Katja; Merz, Jennifer; Mohr, Niklas; Ostapchuk, Andrey; Perieanu, Adrian; Raupach, Frank; Sammet, Jan; Schael, Stefan; Sprenger, Daniel; Weber, Hendrik; Weber, Martin; Wittmer, Bruno; Ata, Metin; Bender, Walter; Dietz-Laursonn, Erik; Erdmann, Martin; Frangenheim, Jens; Hebbeker, Thomas; Hinzmann, Andreas; Hoepfner, Kerstin; Klimkovich, Tatsiana; Klingebiel, Dennis; Kreuzer, Peter; Lanske, Dankfried; Magass, Carsten; Merschmeyer, Markus; Meyer, Arnd; Papacz, Paul; Pieta, Holger; Reithler, Hans; Schmitz, Stefan Antonius; Sonnenschein, Lars; Steggemann, Jan; Teyssier, Daniel; Bontenackels, Michael; Davids, Martina; Duda, Markus; Flügge, Günter; Geenen, Heiko; Giffels, Manuel; Haj Ahmad, Wael; Heydhausen, Dirk; Kress, Thomas; Kuessel, Yvonne; Linn, Alexander; Nowack, Andreas; Perchalla, Lars; Pooth, Oliver; Rennefeld, Jörg; Sauerland, Philip; Stahl, Achim; Thomas, Maarten; Tornier, Daiske; Zoeller, Marc Henning; Aldaya Martin, Maria; Behrenhoff, Wolf; Behrens, Ulf; Bergholz, Matthias; Bethani, Agni; Borras, Kerstin; Cakir, Altan; Campbell, Alan; Castro, Elena; Dammann, Dirk; Eckerlin, Guenter; Eckstein, Doris; Flossdorf, Alexander; Flucke, Gero; Geiser, Achim; Hauk, Johannes; Jung, Hannes; Kasemann, Matthias; Katkov, Igor; Katsas, Panagiotis; Kleinwort, Claus; Kluge, Hannelies; Knutsson, Albert; Krämer, Mira; Krücker, Dirk; Kuznetsova, Ekaterina; Lange, Wolfgang; Lohmann, Wolfgang; Mankel, Rainer; Marienfeld, Markus; Melzer-Pellmann, Isabell-Alissandra; Meyer, Andreas Bernhard; Mnich, Joachim; Mussgiller, Andreas; Olzem, Jan; Pitzl, Daniel; Raspereza, Alexei; Raval, Amita; Rosin, Michele; Schmidt, Ringo; Schoerner-Sadenius, Thomas; Sen, Niladri; Spiridonov, Alexander; Stein, Matthias; Tomaszewska, Justyna; Walsh, Roberval; Wissing, Christoph; Autermann, Christian; Blobel, Volker; Bobrovskyi, Sergei; Draeger, Jula; Enderle, Holger; Gebbert, Ulla; Kaschube, Kolja; Kaussen, Gordon; Klanner, Robert; Lange, Jörn; Mura, Benedikt; Naumann-Emme, Sebastian; Nowak, Friederike; Pietsch, Niklas; Sander, Christian; Schettler, Hannes; Schleper, Peter; Schröder, Matthias; Schum, Torben; Schwandt, Joern; Stadie, Hartmut; Steinbrück, Georg; Thomsen, Jan; Barth, Christian; Bauer, Julia; Buege, Volker; Chwalek, Thorsten; De Boer, Wim; Dierlamm, Alexander; Dirkes, Guido; Feindt, Michael; Gruschke, Jasmin; Hackstein, Christoph; Hartmann, Frank; Heinrich, Michael; Held, Hauke; Hoffmann, Karl-Heinz; Honc, Simon; Komaragiri, Jyothsna Rani; Kuhr, Thomas; Martschei, Daniel; Mueller, Steffen; Müller, Thomas; Niegel, Martin; Oberst, Oliver; Oehler, Andreas; Ott, Jochen; Peiffer, Thomas; Quast, Gunter; Rabbertz, Klaus; Ratnikov, Fedor; Ratnikova, Natalia; Renz, Manuel; Saout, Christophe; Scheurer, Armin; Schieferdecker, Philipp; Schilling, Frank-Peter; Schmanau, Mike; Schott, Gregory; Simonis, Hans-Jürgen; Stober, Fred-Markus Helmut; Troendle, Daniel; Wagner-Kuhr, Jeannine; Weiler, Thomas; Zeise, Manuel; Zhukov, Valery; Ziebarth, Eva Barbara; Daskalakis, Georgios; Geralis, Theodoros; Kesisoglou, Stilianos; Kyriakis, Aristotelis; Loukas, Demetrios; Manolakos, Ioannis; Markou, Athanasios; Markou, Christos; Mavrommatis, Charalampos; Ntomari, Eleni; Petrakou, Eleni; Gouskos, Loukas; Mertzimekis, Theodoros; Panagiotou, Apostolos; Stiliaris, Efstathios; Evangelou, Ioannis; Foudas, Costas; Kokkas, Panagiotis; Manthos, Nikolaos; Papadopoulos, Ioannis; Patras, Vaios; Triantis, Frixos A; Aranyi, Attila; Bencze, Gyorgy; Boldizsar, Laszlo; Hajdu, Csaba; Hidas, Pàl; Horvath, Dezso; Kapusi, Anita; Krajczar, Krisztian; Sikler, Ferenc; Veres, Gabor Istvan; Vesztergombi, Gyorgy; Beni, Noemi; Molnar, Jozsef; Palinkas, Jozsef; Szillasi, Zoltan; Veszpremi, Viktor; Raics, Peter; Trocsanyi, Zoltan Laszlo; Ujvari, Balazs; Bansal, Sunil; Beri, Suman Bala; Bhatnagar, Vipin; Dhingra, Nitish; Gupta, Ruchi; Jindal, Monika; Kaur, Manjit; Kohli, Jatinder Mohan; Mehta, Manuk Zubin; Nishu, Nishu; Saini, Lovedeep Kaur; Sharma, Archana; Singh, Anil; Singh, Jasbir; Singh, Supreet Pal; Ahuja, Sudha; Bhattacharya, Satyaki; Choudhary, Brajesh C; Gupta, Pooja; Jain, Sandhya; Jain, Shilpi; Kumar, Ashok; Ranjan, Kirti; Shivpuri, Ram Krishen; Choudhury, Rajani Kant; Dutta, Dipanwita; Kailas, Swaminathan; Kumar, Vineet; Mohanty, Ajit Kumar; Pant, Lalit Mohan; Shukla, Prashant; Aziz, Tariq; Guchait, Monoranjan; Gurtu, Atul; Maity, Manas; Majumder, Devdatta; Majumder, Gobinda; Mazumdar, Kajari; Mohanty, Gagan Bihari; Saha, Anirban; Sudhakar, Katta; Wickramage, Nadeesha; Banerjee, Sudeshna; Dugad, Shashikant; Mondal, Naba Kumar; Arfaei, Hessamaddin; Bakhshiansohi, Hamed; Etesami, Seyed Mohsen; Fahim, Ali; Hashemi, Majid; Jafari, Abideh; Khakzad, Mohsen; Mohammadi, Abdollah; Mohammadi Najafabadi, Mojtaba; Paktinat Mehdiabadi, Saeid; Safarzadeh, Batool; Zeinali, Maryam; Abbrescia, Marcello; Barbone, Lucia; Calabria, Cesare; Colaleo, Anna; Creanza, Donato; De Filippis, Nicola; De Palma, Mauro; Fiore, Luigi; Iaselli, Giuseppe; Lusito, Letizia; Maggi, Giorgio; Maggi, Marcello; Manna, Norman; Marangelli, Bartolomeo; My, Salvatore; Nuzzo, Salvatore; Pacifico, Nicola; Pierro, Giuseppe Antonio; Pompili, Alexis; Pugliese, Gabriella; Romano, Francesco; Roselli, Giuseppe; Selvaggi, Giovanna; Silvestris, Lucia; Trentadue, Raffaello; Tupputi, Salvatore; Zito, Giuseppe; Abbiendi, Giovanni; Benvenuti, Alberto; Bonacorsi, Daniele; Braibant-Giacomelli, Sylvie; Brigliadori, Luca; Capiluppi, Paolo; Castro, Andrea; Cavallo, Francesca Romana; Cuffiani, Marco; Dallavalle, Gaetano-Marco; Fabbri, Fabrizio; Fanfani, Alessandra; Fasanella, Daniele; Giacomelli, Paolo; Giunta, Marina; Marcellini, Stefano; Masetti, Gianni; Meneghelli, Marco; Montanari, Alessandro; Navarria, Francesco; Odorici, Fabrizio; Perrotta, Andrea; Primavera, Federica; Rossi, Antonio; Rovelli, Tiziano; Siroli, Gianni; Travaglini, Riccardo; Albergo, Sebastiano; Cappello, Gigi; Chiorboli, Massimiliano; Costa, Salvatore; Tricomi, Alessia; Tuve, Cristina; Barbagli, Giuseppe; Ciulli, Vitaliano; Civinini, Carlo; D'Alessandro, Raffaello; Focardi, Ettore; Frosali, Simone; Gallo, Elisabetta; Gonzi, Sandro; Lenzi, Piergiulio; Meschini, Marco; Paoletti, Simone; Sguazzoni, Giacomo; Tropiano, Antonio; Benussi, Luigi; Bianco, Stefano; Colafranceschi, Stefano; Fabbri, Franco; Piccolo, Davide; Fabbricatore, Pasquale; Musenich, Riccardo; Benaglia, Andrea; De Guio, Federico; Di Matteo, Leonardo; Gennai, Simone; Ghezzi, Alessio; Malvezzi, Sandra; Martelli, Arabella; Massironi, Andrea; Menasce, Dario; Moroni, Luigi; Paganoni, Marco; Pedrini, Daniele; Ragazzi, Stefano; Redaelli, Nicola; Sala, Silvano; Tabarelli de Fatis, Tommaso; Buontempo, Salvatore; Carrillo Montoya, Camilo Andres; Cavallo, Nicola; De Cosa, Annapaola; Fabozzi, Francesco; Iorio, Alberto Orso Maria; Lista, Luca; Merola, Mario; Paolucci, Pierluigi; Azzi, Patrizia; Bacchetta, Nicola; Bellan, Paolo; Bisello, Dario; Branca, Antonio; Carlin, Roberto; Checchia, Paolo; De Mattia, Marco; Dorigo, Tommaso; Dosselli, Umberto; Gasparini, Fabrizio; Gasparini, Ugo; Gozzelino, Andrea; Lacaprara, Stefano; Lazzizzera, Ignazio; Margoni, Martino; Mazzucato, Mirco; Meneguzzo, Anna Teresa; Nespolo, Massimo; Perrozzi, Luca; Pozzobon, Nicola; Ronchese, Paolo; Simonetto, Franco; Torassa, Ezio; Tosi, Mia; Triossi, Andrea; Vanini, Sara; Zotto, Pierluigi; Zumerle, Gianni; Baesso, Paolo; Berzano, Umberto; Ratti, Sergio P; Riccardi, Cristina; Torre, Paola; Vitulo, Paolo; Viviani, Claudio; Biasini, Maurizio; Bilei, Gian Mario; Caponeri, Benedetta; Fanò, Livio; Lariccia, Paolo; Lucaroni, Andrea; Mantovani, Giancarlo; Menichelli, Mauro; Nappi, Aniello; Romeo, Francesco; Santocchia, Attilio; Taroni, Silvia; Valdata, Marisa; Azzurri, Paolo; Bagliesi, Giuseppe; Bernardini, Jacopo; Boccali, Tommaso; Broccolo, Giuseppe; Castaldi, Rino; D'Agnolo, Raffaele Tito; Dell'Orso, Roberto; Fiori, Francesco; Foà, Lorenzo; Giassi, Alessandro; Kraan, Aafke; Ligabue, Franco; Lomtadze, Teimuraz; Martini, Luca; Messineo, Alberto; Palla, Fabrizio; Peruzzi, Marco; Segneri, Gabriele; Serban, Alin Titus; Spagnolo, Paolo; Tenchini, Roberto; Tonelli, Guido; Venturi, Andrea; Verdini, Piero Giorgio; Barone, Luciano; Cavallari, Francesca; Del Re, Daniele; Di Marco, Emanuele; Diemoz, Marcella; Franci, Daniele; Grassi, Marco; Longo, Egidio; Nourbakhsh, Shervin; Organtini, Giovanni; Pandolfi, Francesco; Paramatti, Riccardo; Rahatlou, Shahram; Rovelli, Chiara; Amapane, Nicola; Arcidiacono, Roberta; Argiro, Stefano; Arneodo, Michele; Biino, Cristina; Botta, Cristina; Cartiglia, Nicolo; Castello, Roberto; Costa, Marco; Demaria, Natale; Graziano, Alberto; Mariotti, Chiara; Marone, Matteo; Maselli, Silvia; Migliore, Ernesto; Mila, Giorgia; Monaco, Vincenzo; Musich, Marco; Obertino, Maria Margherita; Pastrone, Nadia; Pelliccioni, Mario; Romero, Alessandra; Ruspa, Marta; Sacchi, Roberto; Sola, Valentina; Solano, Ada; Staiano, Amedeo; Vilela Pereira, Antonio; Belforte, Stefano; Cossutti, Fabio; Della Ricca, Giuseppe; Gobbo, Benigno; Montanino, Damiana; Penzo, Aldo; Heo, Seong Gu; Nam, Soon-Kwon; Chang, Sunghyun; Chung, Jin Hyuk; Kim, Dong Hee; Kim, Gui Nyun; Kim, Ji Eun; Kong, Dae Jung; Park, Hyangkyu; Ro, Sang-Ryul; Son, Dohhee; Son, Dong-Chul; Son, Taejin; Kim, Jaeho; Kim, Jae Yool; Song, Sanghyeon; Choi, Suyong; Hong, Byung-Sik; Jeong, Min-Soo; Jo, Mihee; Kim, Hyunchul; Kim, Ji Hyun; Kim, Tae Jeong; Lee, Kyong Sei; Moon, Dong Ho; Park, Sung Keun; Rhee, Han-Bum; Seo, Eunsung; Shin, Seungsu; Sim, Kwang Souk; Choi, Minkyoo; Kang, Seokon; Kim, Hyunyong; Park, Chawon; Park, Inkyu; Park, Sangnam; Ryu, Geonmo; Choi, Young-Il; Choi, Young Kyu; Goh, Junghwan; Kim, Min Suk; Kwon, Eunhyang; Lee, Jongseok; Lee, Sungeun; Seo, Hyunkwan; Yu, Intae; Bilinskas, Mykolas Jurgis; Grigelionis, Ignas; Janulis, Mindaugas; Martisiute, Dalia; Petrov, Pavel; Sabonis, Tomas; Castilla-Valdez, Heriberto; De La Cruz-Burelo, Eduard; Heredia-de La Cruz, Ivan; Lopez-Fernandez, Ricardo; Magaña Villalba, Ricardo; Sánchez-Hernández, Alberto; Villasenor-Cendejas, Luis Manuel; Carrillo Moreno, Salvador; Vazquez Valencia, Fabiola; Salazar Ibarguen, Humberto Antonio; Casimiro Linares, Edgar; Morelos Pineda, Antonio; Reyes-Santos, Marco A; Krofcheck, David; Tam, Jason; Yiu, Chun Hin; Butler, Philip H; Doesburg, Robert; Silverwood, Hamish; Ahmad, Muhammad; Ahmed, Ijaz; Asghar, Muhammad Irfan; Hoorani, Hafeez R; Khan, Wajid Ali; Khurshid, Taimoor; Qazi, Shamona; Brona, Grzegorz; Cwiok, Mikolaj; Dominik, Wojciech; Doroba, Krzysztof; Kalinowski, Artur; Konecki, Marcin; Krolikowski, Jan; Frueboes, Tomasz; Gokieli, Ryszard; Górski, Maciej; Kazana, Malgorzata; Nawrocki, Krzysztof; Romanowska-Rybinska, Katarzyna; Szleper, Michal; Wrochna, Grzegorz; Zalewski, Piotr; Almeida, Nuno; Bargassa, Pedrame; David Tinoco Mendes, Andre; Faccioli, Pietro; Ferreira Parracho, Pedro Guilherme; Gallinaro, Michele; Musella, Pasquale; Nayak, Aruna; Ribeiro, Pedro Quinaz; Seixas, Joao; Varela, Joao; Afanasiev, Serguei; Belotelov, Ivan; Bunin, Pavel; Golutvin, Igor; Kamenev, Alexey; Karjavin, Vladimir; Kozlov, Guennady; Lanev, Alexander; Moisenz, Petr; Palichik, Vladimir; Perelygin, Victor; Shmatov, Sergey; Smirnov, Vitaly; Volodko, Anton; Zarubin, Anatoli; Golovtsov, Victor; Ivanov, Yury; Kim, Victor; Levchenko, Petr; Murzin, Victor; Oreshkin, Vadim; Smirnov, Igor; Sulimov, Valentin; Uvarov, Lev; Vavilov, Sergey; Vorobyev, Alexey; Vorobyev, Andrey; Andreev, Yuri; Dermenev, Alexander; Gninenko, Sergei; Golubev, Nikolai; Kirsanov, Mikhail; Krasnikov, Nikolai; Matveev, Viktor; Pashenkov, Anatoli; Toropin, Alexander; Troitsky, Sergey; Epshteyn, Vladimir; Gavrilov, Vladimir; Kaftanov, Vitali; Kossov, Mikhail; Krokhotin, Andrey; Lychkovskaya, Natalia; Popov, Vladimir; Safronov, Grigory; Semenov, Sergey; Stolin, Viatcheslav; Vlasov, Evgueni; Zhokin, Alexander; Boos, Edouard; Dubinin, Mikhail; Dudko, Lev; Ershov, Alexander; Gribushin, Andrey; Kodolova, Olga; Lokhtin, Igor; Markina, Anastasia; Obraztsov, Stepan; Perfilov, Maxim; Petrushanko, Sergey; Sarycheva, Ludmila; Savrin, Viktor; Snigirev, Alexander; Andreev, Vladimir; Azarkin, Maksim; Dremin, Igor; Kirakosyan, Martin; Leonidov, Andrey; Rusakov, Sergey V; Vinogradov, Alexey; Azhgirey, Igor; Bitioukov, Sergei; Grishin, Viatcheslav; Kachanov, Vassili; Konstantinov, Dmitri; Korablev, Andrey; Krychkine, Victor; Petrov, Vladimir; Ryutin, Roman; Slabospitsky, Sergey; Sobol, Andrei; Tourtchanovitch, Leonid; Troshin, Sergey; Tyurin, Nikolay; Uzunian, Andrey; Volkov, Alexey; Adzic, Petar; Djordjevic, Milos; Krpic, Dragomir; Milosevic, Jovan; Aguilar-Benitez, Manuel; Alcaraz Maestre, Juan; Arce, Pedro; Battilana, Carlo; Calvo, Enrique; Cepeda, Maria; Cerrada, Marcos; Chamizo Llatas, Maria; Colino, Nicanor; De La Cruz, Begona; Delgado Peris, Antonio; Diez Pardos, Carmen; Domínguez Vázquez, Daniel; Fernandez Bedoya, Cristina; Fernández Ramos, Juan Pablo; Ferrando, Antonio; Flix, Jose; Fouz, Maria Cruz; Garcia-Abia, Pablo; Gonzalez Lopez, Oscar; Goy Lopez, Silvia; Hernandez, Jose M; Josa, Maria Isabel; Merino, Gonzalo; Puerta Pelayo, Jesus; Redondo, Ignacio; Romero, Luciano; Santaolalla, Javier; Senghi Soares, Mara; Willmott, Carlos; Albajar, Carmen; Codispoti, Giuseppe; de Trocóniz, Jorge F; Cuevas, Javier; Fernandez Menendez, Javier; Folgueras, Santiago; Gonzalez Caballero, Isidro; Lloret Iglesias, Lara; Vizan Garcia, Jesus Manuel; Brochero Cifuentes, Javier Andres; Cabrillo, Iban Jose; Calderon, Alicia; Chuang, Shan-Huei; Duarte Campderros, Jordi; Felcini, Marta; Fernandez, Marcos; Gomez, Gervasio; Gonzalez Sanchez, Javier; Jorda, Clara; Lobelle Pardo, Patricia; Lopez Virto, Amparo; Marco, Jesus; Marco, Rafael; Martinez Rivero, Celso; Matorras, Francisco; Munoz Sanchez, Francisca Javiela; Piedra Gomez, Jonatan; Rodrigo, Teresa; Rodríguez-Marrero, Ana Yaiza; Ruiz-Jimeno, Alberto; Scodellaro, Luca; Sobron Sanudo, Mar; Vila, Ivan; Vilar Cortabitarte, Rocio; Abbaneo, Duccio; Auffray, Etiennette; Auzinger, Georg; Baillon, Paul; Ball, Austin; Barney, David; Bell, Alan James; Benedetti, Daniele; Bernet, Colin; Bialas, Wojciech; Bloch, Philippe; Bocci, Andrea; Bolognesi, Sara; Bona, Marcella; Breuker, Horst; Bunkowski, Karol; Camporesi, Tiziano; Cerminara, Gianluca; Coarasa Perez, Jose Antonio; Curé, Benoît; D'Enterria, David; De Roeck, Albert; Di Guida, Salvatore; Dupont-Sagorin, Niels; Elliott-Peisert, Anna; Frisch, Benjamin; Funk, Wolfgang; Gaddi, Andrea; Georgiou, Georgios; Gerwig, Hubert; Gigi, Dominique; Gill, Karl; Giordano, Domenico; Glege, Frank; Gomez-Reino Garrido, Robert; Gouzevitch, Maxime; Govoni, Pietro; Gowdy, Stephen; Guiducci, Luigi; Hansen, Magnus; Hartl, Christian; Harvey, John; Hegeman, Jeroen; Hegner, Benedikt; Hoffmann, Hans Falk; Honma, Alan; Innocente, Vincenzo; Janot, Patrick; Kaadze, Ketino; Karavakis, Edward; Lecoq, Paul; Lourenco, Carlos; Maki, Tuula; Malberti, Martina; Malgeri, Luca; Mannelli, Marcello; Masetti, Lorenzo; Maurisset, Aurelie; Meijers, Frans; Mersi, Stefano; Meschi, Emilio; Moser, Roland; Mozer, Matthias Ulrich; Mulders, Martijn; Nesvold, Erik; Nguyen, Matthew; Orimoto, Toyoko; Orsini, Luciano; Perez, Emmanuelle; Petrilli, Achille; Pfeiffer, Andreas; Pierini, Maurizio; Pimiä, Martti; Piparo, Danilo; Polese, Giovanni; Racz, Attila; Rodrigues Antunes, Joao; Rolandi, Gigi; Rommerskirchen, Tanja; Rovere, Marco; Sakulin, Hannes; Schäfer, Christoph; Schwick, Christoph; Segoni, Ilaria; Sharma, Archana; Siegrist, Patrice; Simon, Michal; Sphicas, Paraskevas; Spiropulu, Maria; Stoye, Markus; Tadel, Matevz; Tropea, Paola; Tsirou, Andromachi; Vichoudis, Paschalis; Voutilainen, Mikko; Zeuner, Wolfram Dietrich; Bertl, Willi; Deiters, Konrad; Erdmann, Wolfram; Gabathuler, Kurt; Horisberger, Roland; Ingram, Quentin; Kaestli, Hans-Christian; König, Stefan; Kotlinski, Danek; Langenegger, Urs; Meier, Frank; Renker, Dieter; Rohe, Tilman; Sibille, Jennifer; Starodumov, Andrei; Bortignon, Pierluigi; Caminada, Lea; Chanon, Nicolas; Chen, Zhiling; Cittolin, Sergio; Dissertori, Günther; Dittmar, Michael; Eugster, Jürg; Freudenreich, Klaus; Grab, Christoph; Hervé, Alain; Hintz, Wieland; Lecomte, Pierre; Lustermann, Werner; Marchica, Carmelo; Martinez Ruiz del Arbol, Pablo; Meridiani, Paolo; Milenovic, Predrag; Moortgat, Filip; Nägeli, Christoph; Nef, Pascal; Nessi-Tedaldi, Francesca; Pape, Luc; Pauss, Felicitas; Punz, Thomas; Rizzi, Andrea; Ronga, Frederic Jean; Rossini, Marco; Sala, Leonardo; Sanchez, Ann - Karin; Sawley, Marie-Christine; Stieger, Benjamin; Tauscher, Ludwig; Thea, Alessandro; Theofilatos, Konstantinos; Treille, Daniel; Urscheler, Christina; Wallny, Rainer; Weber, Matthias; Wehrli, Lukas; Weng, Joanna; Aguilo, Ernest; Amsler, Claude; Chiochia, Vincenzo; De Visscher, Simon; Favaro, Carlotta; Ivova Rikova, Mirena; Millan Mejias, Barbara; Otiougova, Polina; Regenfus, Christian; Robmann, Peter; Schmidt, Alexander; Snoek, Hella; Chang, Yuan-Hann; Chen, Kuan-Hsin; Dutta, Suchandra; Kuo, Chia-Ming; Li, Syue-Wei; Lin, Willis; Liu, Zong-Kai; Lu, Yun-Ju; Mekterovic, Darko; Volpe, Roberta; Wu, Jing-Han; Yu, Shin-Shan; Bartalini, Paolo; Chang, Paoti; Chang, You-Hao; Chang, Yu-Wei; Chao, Yuan; Chen, Kai-Feng; Hou, George Wei-Shu; Hsiung, Yee; Kao, Kai-Yi; Lei, Yeong-Jyi; Lu, Rong-Shyang; Shiu, Jing-Ge; Tzeng, Yeng-Ming; Wang, Minzu; Adiguzel, Aytul; Bakirci, Mustafa Numan; Cerci, Salim; Dozen, Candan; Dumanoglu, Isa; Eskut, Eda; Girgis, Semiray; Gokbulut, Gul; Hos, Ilknur; Kangal, Evrim Ersin; Kayis Topaksu, Aysel; Onengut, Gulsen; Ozdemir, Kadri; Ozturk, Sertac; Polatoz, Ayse; Sogut, Kenan; Sunar Cerci, Deniz; Tali, Bayram; Topakli, Huseyin; Uzun, Dilber; Vergili, Latife Nukhet; Vergili, Mehmet; Akin, Ilina Vasileva; Aliev, Takhmasib; Bilmis, Selcuk; Deniz, Muhammed; Gamsizkan, Halil; Guler, Ali Murat; Ocalan, Kadir; Ozpineci, Altug; Serin, Meltem; Sever, Ramazan; Surat, Ugur Emrah; Yildirim, Eda; Zeyrek, Mehmet; Deliomeroglu, Mehmet; Demir, Durmus; Gülmez, Erhan; Isildak, Bora; Kaya, Mithat; Kaya, Ozlem; Ozkorucuklu, Suat; Sonmez, Nasuf; Levchuk, Leonid; Bostock, Francis; Brooke, James John; Cheng, Teh Lee; Clement, Emyr; Cussans, David; Frazier, Robert; Goldstein, Joel; Grimes, Mark; Hansen, Maria; Hartley, Dominic; Heath, Greg P; Heath, Helen F; Kreczko, Lukasz; Metson, Simon; Newbold, Dave M; Nirunpong, Kachanon; Poll, Anthony; Senkin, Sergey; Smith, Vincent J; Ward, Simon; Basso, Lorenzo; Bell, Ken W; Belyaev, Alexander; Brew, Christopher; Brown, Robert M; Camanzi, Barbara; Cockerill, David JA; Coughlan, John A; Harder, Kristian; Harper, Sam; Jackson, James; Kennedy, Bruce W; Olaiya, Emmanuel; Petyt, David; Radburn-Smith, Benjamin Charles; Shepherd-Themistocleous, Claire; Tomalin, Ian R; Womersley, William John; Worm, Steven; Bainbridge, Robert; Ball, Gordon; Ballin, Jamie; Beuselinck, Raymond; Buchmuller, Oliver; Colling, David; Cripps, Nicholas; Cutajar, Michael; Davies, Gavin; Della Negra, Michel; Ferguson, William; Fulcher, Jonathan; Futyan, David; Gilbert, Andrew; Guneratne Bryer, Arlo; Hall, Geoffrey; Hatherell, Zoe; Hays, Jonathan; Iles, Gregory; Jarvis, Martyn; Karapostoli, Georgia; Lyons, Louis; MacEvoy, Barry C; Magnan, Anne-Marie; Marrouche, Jad; Mathias, Bryn; Nandi, Robin; Nash, Jordan; Nikitenko, Alexander; Papageorgiou, Anastasios; Pesaresi, Mark; Petridis, Konstantinos; Pioppi, Michele; Raymond, David Mark; Rogerson, Samuel; Rompotis, Nikolaos; Rose, Andrew; Ryan, Matthew John; Seez, Christopher; Sharp, Peter; Sparrow, Alex; Tapper, Alexander; Tourneur, Stephane; Vazquez Acosta, Monica; Virdee, Tejinder; Wakefield, Stuart; Wardle, Nicholas; Wardrope, David; Whyntie, Tom; Barrett, Matthew; Chadwick, Matthew; Cole, Joanne; Hobson, Peter R; Khan, Akram; Kyberd, Paul; Leslie, Dawn; Martin, William; Reid, Ivan; Teodorescu, Liliana; Hatakeyama, Kenichi; Bose, Tulika; Carrera Jarrin, Edgar; Fantasia, Cory; Heister, Arno; St John, Jason; Lawson, Philip; Lazic, Dragoslav; Rohlf, James; Sperka, David; Sulak, Lawrence; Avetisyan, Aram; Bhattacharya, Saptaparna; Chou, John Paul; Cutts, David; Ferapontov, Alexey; Heintz, Ulrich; Jabeen, Shabnam; Kukartsev, Gennadiy; Landsberg, Greg; Narain, Meenakshi; Nguyen, Duong; Segala, Michael; Sinthuprasith, Tutanon; Speer, Thomas; Tsang, Ka Vang; Breedon, Richard; Calderon De La Barca Sanchez, Manuel; Chauhan, Sushil; Chertok, Maxwell; Conway, John; Cox, Peter Timothy; Dolen, James; Erbacher, Robin; Friis, Evan; Ko, Winston; Kopecky, Alexandra; Lander, Richard; Liu, Haidong; Maruyama, Sho; Miceli, Tia; Nikolic, Milan; Pellett, Dave; Robles, Jorge; Salur, Sevil; Schwarz, Thomas; Searle, Matthew; Smith, John; Squires, Michael; Tripathi, Mani; Vasquez Sierra, Ricardo; Veelken, Christian; Andreev, Valeri; Arisaka, Katsushi; Cline, David; Cousins, Robert; Deisher, Amanda; Duris, Joseph; Erhan, Samim; Farrell, Chris; Hauser, Jay; Ignatenko, Mikhail; Jarvis, Chad; Plager, Charles; Rakness, Gregory; Schlein, Peter; Tucker, Jordan; Valuev, Vyacheslav; Babb, John; Chandra, Avdhesh; Clare, Robert; Ellison, John Anthony; Gary, J William; Giordano, Ferdinando; Hanson, Gail; Jeng, Geng-Yuan; Kao, Shih-Chuan; Liu, Feng; Liu, Hongliang; Long, Owen Rosser; Luthra, Arun; Nguyen, Harold; Shen, Benjamin C; Stringer, Robert; Sturdy, Jared; Sumowidagdo, Suharyo; Wilken, Rachel; Wimpenny, Stephen; Andrews, Warren; Branson, James G; Cerati, Giuseppe Benedetto; Sudano, Elizabeth; Evans, David; Golf, Frank; Holzner, André; Kelley, Ryan; Lebourgeois, Matthew; Letts, James; Mangano, Boris; Padhi, Sanjay; Palmer, Christopher; Petrucciani, Giovanni; Pi, Haifeng; Pieri, Marco; Ranieri, Riccardo; Sani, Matteo; Sharma, Vivek; Simon, Sean; Tu, Yanjun; Vartak, Adish; Wasserbaech, Steven; Würthwein, Frank; Yagil, Avraham; Yoo, Jaehyeok; Barge, Derek; Bellan, Riccardo; Campagnari, Claudio; D'Alfonso, Mariarosaria; Danielson, Thomas; Flowers, Kristen; Geffert, Paul; Incandela, Joe; Justus, Christopher; Kalavase, Puneeth; Koay, Sue Ann; Kovalskyi, Dmytro; Krutelyov, Vyacheslav; Lowette, Steven; Mccoll, Nickolas; Pavlunin, Viktor; Rebassoo, Finn; Ribnik, Jacob; Richman, Jeffrey; Rossin, Roberto; Stuart, David; To, Wing; Vlimant, Jean-Roch; Apresyan, Artur; Bornheim, Adolf; Bunn, Julian; Chen, Yi; Gataullin, Marat; Ma, Yousi; Mott, Alexander; Newman, Harvey B; Rogan, Christopher; Shin, Kyoungha; Timciuc, Vladlen; Traczyk, Piotr; Veverka, Jan; Wilkinson, Richard; Yang, Yong; Zhu, Ren-Yuan; Akgun, Bora; Carroll, Ryan; Ferguson, Thomas; Iiyama, Yutaro; Jang, Dong Wook; Jun, Soon Yung; Liu, Yueh-Feng; Paulini, Manfred; Russ, James; Vogel, Helmut; Vorobiev, Igor; Cumalat, John Perry; Dinardo, Mauro Emanuele; Drell, Brian Robert; Edelmaier, Christopher; Ford, William T; Gaz, Alessandro; Heyburn, Bernadette; Luiggi Lopez, Eduardo; Nauenberg, Uriel; Smith, James; Stenson, Kevin; Ulmer, Keith; Wagner, Stephen Robert; Zang, Shi-Lei; Agostino, Lorenzo; Alexander, James; Cassel, David; Chatterjee, Avishek; Das, Souvik; Eggert, Nicholas; Gibbons, Lawrence Kent; Heltsley, Brian; Hopkins, Walter; Khukhunaishvili, Aleko; Kreis, Benjamin; Nicolas Kaufman, Gala; Patterson, Juliet Ritchie; Puigh, Darren; Ryd, Anders; Salvati, Emmanuele; Shi, Xin; Sun, Werner; Teo, Wee Don; Thom, Julia; Thompson, Joshua; Vaughan, Jennifer; Weng, Yao; Winstrom, Lucas; Wittich, Peter; Biselli, Angela; Cirino, Guy; Winn, Dave; Abdullin, Salavat; Albrow, Michael; Anderson, Jacob; Apollinari, Giorgio; Atac, Muzaffer; Bakken, Jon Alan; Banerjee, Sunanda; Bauerdick, Lothar AT; Beretvas, Andrew; Berryhill, Jeffrey; Bhat, Pushpalatha C; Bloch, Ingo; Borcherding, Frederick; Burkett, Kevin; Butler, Joel Nathan; Chetluru, Vasundhara; Cheung, Harry; Chlebana, Frank; Cihangir, Selcuk; Cooper, William; Eartly, David P; Elvira, Victor Daniel; Esen, Selda; Fisk, Ian; Freeman, Jim; Gao, Yanyan; Gottschalk, Erik; Green, Dan; Gunthoti, Kranti; Gutsche, Oliver; Hanlon, Jim; Harris, Robert M; Hirschauer, James; Hooberman, Benjamin; Jensen, Hans; Johnson, Marvin; Joshi, Umesh; Khatiwada, Rakshya; Klima, Boaz; Kousouris, Konstantinos; Kunori, Shuichi; Kwan, Simon; Leonidopoulos, Christos; Limon, Peter; Lincoln, Don; Lipton, Ron; Lykken, Joseph; Maeshima, Kaori; Marraffino, John Michael; Mason, David; McBride, Patricia; Miao, Ting; Mishra, Kalanand; Mrenna, Stephen; Musienko, Yuri; Newman-Holmes, Catherine; O'Dell, Vivian; Pordes, Ruth; Prokofyev, Oleg; Saoulidou, Niki; Sexton-Kennedy, Elizabeth; Sharma, Seema; Spalding, William J; Spiegel, Leonard; Tan, Ping; Taylor, Lucas; Tkaczyk, Slawek; Uplegger, Lorenzo; Vaandering, Eric Wayne; Vidal, Richard; Whitmore, Juliana; Wu, Weimin; Yang, Fan; Yumiceva, Francisco; Yun, Jae Chul; Acosta, Darin; Avery, Paul; Bourilkov, Dimitri; Chen, Mingshui; De Gruttola, Michele; Di Giovanni, Gian Piero; Dobur, Didar; Drozdetskiy, Alexey; Field, Richard D; Fisher, Matthew; Fu, Yu; Furic, Ivan-Kresimir; Gartner, Joseph; Kim, Bockjoo; Konigsberg, Jacobo; Korytov, Andrey; Kropivnitskaya, Anna; Kypreos, Theodore; Matchev, Konstantin; Mitselmakher, Guenakh; Muniz, Lana; Prescott, Craig; Remington, Ronald; Schmitt, Michael Houston; Scurlock, Bobby; Sellers, Paul; Skhirtladze, Nikoloz; Snowball, Matthew; Wang, Dayong; Yelton, John; Zakaria, Mohammed; Ceron, Cristobal; Gaultney, Vanessa; Kramer, Laird; Lebolo, Luis Miguel; Linn, Stephan; Markowitz, Pete; Martinez, German; Mesa, Dalgis; Rodriguez, Jorge Luis; Adams, Todd; Askew, Andrew; Bochenek, Joseph; Chen, Jie; Diamond, Brendan; Gleyzer, Sergei V; Haas, Jeff; Hagopian, Sharon; Hagopian, Vasken; Jenkins, Merrill; Johnson, Kurtis F; Prosper, Harrison; Quertenmont, Loic; Sekmen, Sezen; Veeraraghavan, Venkatesh; Baarmand, Marc M; Dorney, Brian; Guragain, Samir; Hohlmann, Marcus; Kalakhety, Himali; Ralich, Robert; Vodopiyanov, Igor; Adams, Mark Raymond; Anghel, Ioana Maria; Apanasevich, Leonard; Bai, Yuting; Bazterra, Victor Eduardo; Betts, Russell Richard; Callner, Jeremy; Cavanaugh, Richard; Dragoiu, Cosmin; Gauthier, Lucie; Gerber, Cecilia Elena; Hamdan, Saleh; Hofman, David Jonathan; Khalatyan, Samvel; Kunde, Gerd J; Lacroix, Florent; Malek, Magdalena; O'Brien, Christine; Silvestre, Catherine; Smoron, Agata; Strom, Derek; Varelas, Nikos; Akgun, Ugur; Albayrak, Elif Asli; Bilki, Burak; Clarida, Warren; Duru, Firdevs; Lae, Chung Khim; McCliment, Edward; Merlo, Jean-Pierre; Mermerkaya, Hamit; Mestvirishvili, Alexi; Moeller, Anthony; Nachtman, Jane; Newsom, Charles Ray; Norbeck, Edwin; Olson, Jonathan; Onel, Yasar; Ozok, Ferhat; Sen, Sercan; Wetzel, James; Yetkin, Taylan; Yi, Kai; Barnett, Bruce Arnold; Blumenfeld, Barry; Bonato, Alessio; Eskew, Christopher; Fehling, David; Giurgiu, Gavril; Gritsan, Andrei; Guo, Zijin; Hu, Guofan; Maksimovic, Petar; Rappoccio, Salvatore; Swartz, Morris; Tran, Nhan Viet; Whitbeck, Andrew; Baringer, Philip; Bean, Alice; Benelli, Gabriele; Grachov, Oleg; Kenny Iii, Raymond Patrick; Murray, Michael; Noonan, Daniel; Sanders, Stephen; Wood, Jeffrey Scott; Zhukova, Victoria; Barfuss, Anne-fleur; Bolton, Tim; Chakaberia, Irakli; Ivanov, Andrew; Khalil, Sadia; Makouski, Mikhail; Maravin, Yurii; Shrestha, Shruti; Svintradze, Irakli; Wan, Zongru; Gronberg, Jeffrey; Lange, David; Wright, Douglas; Baden, Drew; Boutemeur, Madjid; Eno, Sarah Catherine; Ferencek, Dinko; Gomez, Jaime; Hadley, Nicholas John; Kellogg, Richard G; Kirn, Malina; Lu, Ying; Mignerey, Alice; Rossato, Kenneth; Rumerio, Paolo; Santanastasio, Francesco; Skuja, Andris; Temple, Jeffrey; Tonjes, Marguerite; Tonwar, Suresh C; Twedt, Elizabeth; Alver, Burak; Bauer, Gerry; Bendavid, Joshua; Busza, Wit; Butz, Erik; Cali, Ivan Amos; Chan, Matthew; Dutta, Valentina; Everaerts, Pieter; Gomez Ceballos, Guillelmo; Goncharov, Maxim; Hahn, Kristan Allan; Harris, Philip; Kim, Yongsun; Klute, Markus; Lee, Yen-Jie; Li, Wei; Loizides, Constantinos; Luckey, Paul David; Ma, Teng; Nahn, Steve; Paus, Christoph; Ralph, Duncan; Roland, Christof; Roland, Gunther; Rudolph, Matthew; Stephans, George; Stöckli, Fabian; Sumorok, Konstanty; Sung, Kevin; Wenger, Edward Allen; Xie, Si; Yang, Mingming; Yilmaz, Yetkin; Yoon, Sungho; Zanetti, Marco; Cooper, Seth; Cushman, Priscilla; Dahmes, Bryan; De Benedetti, Abraham; Dudero, Phillip Russell; Franzoni, Giovanni; Haupt, Jason; Klapoetke, Kevin; Kubota, Yuichi; Mans, Jeremy; Rekovic, Vladimir; Rusack, Roger; Sasseville, Michael; Singovsky, Alexander; Cremaldi, Lucien Marcus; Godang, Romulus; Kroeger, Rob; Perera, Lalith; Rahmat, Rahmat; Sanders, David A; Summers, Don; Bloom, Kenneth; Bose, Suvadeep; Butt, Jamila; Claes, Daniel R; Dominguez, Aaron; Eads, Michael; Keller, Jason; Kelly, Tony; Kravchenko, Ilya; Lazo-Flores, Jose; Malbouisson, Helena; Malik, Sudhir; Snow, Gregory R; Baur, Ulrich; Godshalk, Andrew; Iashvili, Ia; Jain, Supriya; Kharchilava, Avto; Kumar, Ashish; Shipkowski, Simon Peter; Smith, Kenneth; Alverson, George; Barberis, Emanuela; Baumgartel, Darin; Boeriu, Oana; Chasco, Matthew; Reucroft, Steve; Swain, John; Trocino, Daniele; Wood, Darien; Zhang, Jinzhong; Anastassov, Anton; Kubik, Andrew; Odell, Nathaniel; Ofierzynski, Radoslaw Adrian; Pollack, Brian; Pozdnyakov, Andrey; Schmitt, Michael Henry; Stoynev, Stoyan; Velasco, Mayda; Won, Steven; Antonelli, Louis; Berry, Douglas; Hildreth, Michael; Jessop, Colin; Karmgard, Daniel John; Kolb, Jeff; Kolberg, Ted; Lannon, Kevin; Luo, Wuming; Lynch, Sean; Marinelli, Nancy; Morse, David Michael; Pearson, Tessa; Ruchti, Randy; Slaunwhite, Jason; Valls, Nil; Wayne, Mitchell; Ziegler, Jill; Bylsma, Ben; Durkin, Lloyd Stanley; Gu, Jianhui; Hill, Christopher; Killewald, Phillip; Kotov, Khristian; Ling, Ta-Yung; Rodenburg, Marissa; Williams, Grayson; Adam, Nadia; Berry, Edmund; Elmer, Peter; Gerbaudo, Davide; Halyo, Valerie; Hebda, Philip; Hunt, Adam; Jones, John; Laird, Edward; Lopes Pegna, David; Marlow, Daniel; Medvedeva, Tatiana; Mooney, Michael; Olsen, James; Piroué, Pierre; Quan, Xiaohang; Saka, Halil; Stickland, David; Tully, Christopher; Werner, Jeremy Scott; Zuranski, Andrzej; Acosta, Jhon Gabriel; Huang, Xing Tao; Lopez, Angel; Mendez, Hector; Oliveros, Sandra; Ramirez Vargas, Juan Eduardo; Zatserklyaniy, Andriy; Alagoz, Enver; Barnes, Virgil E; Bolla, Gino; Borrello, Laura; Bortoletto, Daniela; Everett, Adam; Garfinkel, Arthur F; Gutay, Laszlo; Hu, Zhen; Jones, Matthew; Koybasi, Ozhan; Kress, Matthew; Laasanen, Alvin T; Leonardo, Nuno; Liu, Chang; Maroussov, Vassili; Merkel, Petra; Miller, David Harry; Neumeister, Norbert; Shipsey, Ian; Silvers, David; Svyatkovskiy, Alexey; Yoo, Hwi Dong; Zablocki, Jakub; Zheng, Yu; Jindal, Pratima; Parashar, Neeti; Boulahouache, Chaouki; Cuplov, Vesna; Ecklund, Karl Matthew; Geurts, Frank JM; Padley, Brian Paul; Redjimi, Radia; Roberts, Jay; Zabel, James; Betchart, Burton; Bodek, Arie; Chung, Yeon Sei; Covarelli, Roberto; de Barbaro, Pawel; Demina, Regina; Eshaq, Yossof; Flacher, Henning; Garcia-Bellido, Aran; Goldenzweig, Pablo; Gotra, Yury; Han, Jiyeon; Harel, Amnon; Miner, Daniel Carl; Orbaker, Douglas; Petrillo, Gianluca; Vishnevskiy, Dmitry; Zielinski, Marek; Bhatti, Anwar; Ciesielski, Robert; Demortier, Luc; Goulianos, Konstantin; Lungu, Gheorghe; Malik, Sarah; Mesropian, Christina; Yan, Ming; Atramentov, Oleksiy; Barker, Anthony; Duggan, Daniel; Gershtein, Yuri; Gray, Richard; Halkiadakis, Eva; Hidas, Dean; Hits, Dmitry; Lath, Amitabh; Panwalkar, Shruti; Patel, Rishi; Richards, Alan; Rose, Keith; Schnetzer, Steve; Somalwar, Sunil; Stone, Robert; Thomas, Scott; Cerizza, Giordano; Hollingsworth, Matthew; Spanier, Stefan; Yang, Zong-Chang; York, Andrew; Eusebi, Ricardo; Gilmore, Jason; Gurrola, Alfredo; Kamon, Teruki; Khotilovich, Vadim; Montalvo, Roy; Osipenkov, Ilya; Pakhotin, Yuriy; Pivarski, James; Safonov, Alexei; Sengupta, Sinjini; Tatarinov, Aysen; Toback, David; Weinberger, Michael; Akchurin, Nural; Bardak, Cemile; Damgov, Jordan; Jeong, Chiyoung; Kovitanggoon, Kittikul; Lee, Sung Won; Mane, Poonam; Roh, Youn; Sill, Alan; Volobouev, Igor; Wigmans, Richard; Yazgan, Efe; Appelt, Eric; Brownson, Eric; Engh, Daniel; Florez, Carlos; Gabella, William; Issah, Michael; Johns, Willard; Kurt, Pelin; Maguire, Charles; Melo, Andrew; Sheldon, Paul; Snook, Benjamin; Tuo, Shengquan; Velkovska, Julia; Arenton, Michael Wayne; Balazs, Michael; Boutle, Sarah; Cox, Bradley; Francis, Brian; Hirosky, Robert; Ledovskoy, Alexander; Lin, Chuanzhe; Neu, Christopher; Yohay, Rachel; Gollapinni, Sowjanya; Harr, Robert; Karchin, Paul Edmund; Lamichhane, Pramod; Mattson, Mark; Milstène, Caroline; Sakharov, Alexandre; Anderson, Michael; Bachtis, Michail; Bellinger, James Nugent; Carlsmith, Duncan; Dasu, Sridhara; Efron, Jonathan; Flood, Kevin; Gray, Lindsey; Grogg, Kira Suzanne; Grothe, Monika; Hall-Wilton, Richard; Herndon, Matthew; Klabbers, Pamela; Klukas, Jeffrey; Lanaro, Armando; Lazaridis, Christos; Leonard, Jessica; Loveless, Richard; Mohapatra, Ajit; Palmonari, Francesco; Reeder, Don; Ross, Ian; Savin, Alexander; Smith, Wesley H; Swanson, Joshua; Weinberg, Marc

2011-01-01

A first measurement of the polarization of W bosons with large transverse momenta in pp collisions is presented. The measurement is based on 36 inverse picobarns of data recorded at sqrt(s) = 7 TeV by the CMS detector at the LHC. The left-handed, right-handed and longitudinal polarization fractions (f_L, f_R, f_0) of W bosons with transverse momenta larger than 50 GeV are determined using decays to both electrons and muons. The muon final state yields the most precise measurement, (f_L - f_R) = 0.240 ± 0.036 (stat.) ± 0.031 (syst.) and f_0 = 0.183 ± 0.087 (stat.) ± 0.123 (syst.) for negatively charged W bosons, and (f_L - f_R) = 0.310 ± 0.036 (stat.) ± 0.017 (syst.) and f_0 = 0.171 ± 0.085 (stat.) ± 0.099 (syst.) for positively charged W bosons. This establishes, for the first time, that W bosons produced in pp collisions with large transverse momenta are predominantly left-handed, as expected in the standard model.

A picosecond widely tunable deep-ultraviolet laser for angle-resolved photoemission spectroscopy

International Nuclear Information System (INIS)

Zhang Feng-Feng; Yang Feng; Zhang Shen-Jin; Xu Zhi; Wang Zhi-Min; Xu Feng-Liang; Peng Qin-Jun; Zhang Jing-Yuan; Xu Zu-Yan; Wang Xiao-Yang; Chen Chuang-Tian

2013-01-01

We develop a picosecond widely tunable laser in a deep-ultraviolet region from 175 nm to 210 nm, generated by two stages of frequency doubling of a 80-MHz mode-locked picosecond Ti:sapphire laser. A β-BaB 2 O 4 walk-off compensation configuration and a KBe 2 BO 3 F 2 prism-coupled device are adopted for the generation of second harmonic and fourth harmonics, respectively. The highest power is 3.72 mW at 193 nm, and the fluctuation at 2.85 mW in 130 min is less than ±2%

Moving object detection in video satellite image based on deep learning

Science.gov (United States)

Zhang, Xueyang; Xiang, Junhua

2017-11-01

Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

The Contribution of F.W. Taylor to Industrial and Organizational Psychology

Directory of Open Access Journals (Sweden)

E. C. Thomas

1982-11-01

In die artikel word getoon dat F.W. Taylor die erkende "vader van wetenskaplike bestuur" ook erkenning behoort te geniet as grondlegger van die Bedryf en -organisasiesielkunde. Sy werk op die terreine van prestasiemotivering en tevredenheid, opleiding, plasing van werkers, bestuurs- en organisasieontwikkeling en arbeidsverhoudinge het waarskynlik die werk van erkende sielkundiges op hierdie gebiede vooruitgeloop, of grondslag daarvoor gelê. Daar word tot die slotsom gekom dat alhoewel Taylor nie 'n opgeleide sielkundige was nie, hy en sy kollegas erkenning moet kry vir die praktiese implimentering van die beginsels en teorieë van die moderne Bedryf- en Organisasiesielkunde.

El espacio y el tiempo en W. F. Flórez

OpenAIRE

Mandianes Castro, Manuel

1990-01-01

En las novelas de W. F. Flórez, como en la vida real de las aldeas gallegas, el espacio tiene muchos niveles: "lareira", aldea, parroquia, ciudad, Castilla, América, "fraga" y el subterráneo maravilloso, y el tiempo tiene cualidades: olor, sabor. Una lectura atenta de estas dos novelas demuestra que la oralidad forma parte importante de la narración; que el autor es, por lo mismo, en buena medida, un auténtico trasmisor de la tradición y que, por lo tanto, los etnólogos y antropólogos han de ...

Fluorinated Polyurethane Scaffolds for 19F Magnetic Resonance Imaging

NARCIS (Netherlands)

Lammers, Twan; Mertens, Marianne E.; Schuster, Philipp; Rahimi, Khosrow; Shi, Yang; Schulz, Volkmar; Kuehne, Alexander J.C.; Jockenhoevel, Stefan; Kiessling, Fabian

2017-01-01

Researchers used fluorinated polyurethane scaffolds for 19F magnetic resonance imaging. They generated a novel fluorinated polymer based on thermoplastic polyurethane (19F -TPU) which possesses distinct properties rendering it suitable for fluorine-based MRI. The 19F -TPU is synthesized from a

Constrained Deep Weak Supervision for Histopathology Image Segmentation.

Science.gov (United States)

Jia, Zhipeng; Huang, Xingyi; Chang, Eric I-Chao; Xu, Yan

2017-11-01

In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Science.gov (United States)

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy

Science.gov (United States)

Ben Arous, Juliette; Binding, Jonas; Léger, Jean-François; Casado, Mariano; Topilko, Piotr; Gigan, Sylvain; Claude Boccara, A.; Bourdieu, Laurent

2011-11-01

Myelin sheath disruption is responsible for multiple neuropathies in the central and peripheral nervous system. Myelin imaging has thus become an important diagnosis tool. However, in vivo imaging has been limited to either low-resolution techniques unable to resolve individual fibers or to low-penetration imaging of single fibers, which cannot provide quantitative information about large volumes of tissue, as required for diagnostic purposes. Here, we perform myelin imaging without labeling and at micron-scale resolution with >300-μm penetration depth on living rodents. This was achieved with a prototype [termed deep optical coherence microscopy (deep-OCM)] of a high-numerical aperture infrared full-field optical coherence microscope, which includes aberration correction for the compensation of refractive index mismatch and high-frame-rate interferometric measurements. We were able to measure the density of individual myelinated fibers in the rat cortex over a large volume of gray matter. In the peripheral nervous system, deep-OCM allows, after minor surgery, in situ imaging of single myelinated fibers over a large fraction of the sciatic nerve. This allows quantitative comparison of normal and Krox20 mutant mice, in which myelination in the peripheral nervous system is impaired. This opens promising perspectives for myelin chronic imaging in demyelinating diseases and for minimally invasive medical diagnosis.

Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy.

Science.gov (United States)

Ben Arous, Juliette; Binding, Jonas; Léger, Jean-François; Casado, Mariano; Topilko, Piotr; Gigan, Sylvain; Boccara, A Claude; Bourdieu, Laurent

2011-11-01

Myelin sheath disruption is responsible for multiple neuropathies in the central and peripheral nervous system. Myelin imaging has thus become an important diagnosis tool. However, in vivo imaging has been limited to either low-resolution techniques unable to resolve individual fibers or to low-penetration imaging of single fibers, which cannot provide quantitative information about large volumes of tissue, as required for diagnostic purposes. Here, we perform myelin imaging without labeling and at micron-scale resolution with >300-μm penetration depth on living rodents. This was achieved with a prototype [termed deep optical coherence microscopy (deep-OCM)] of a high-numerical aperture infrared full-field optical coherence microscope, which includes aberration correction for the compensation of refractive index mismatch and high-frame-rate interferometric measurements. We were able to measure the density of individual myelinated fibers in the rat cortex over a large volume of gray matter. In the peripheral nervous system, deep-OCM allows, after minor surgery, in situ imaging of single myelinated fibers over a large fraction of the sciatic nerve. This allows quantitative comparison of normal and Krox20 mutant mice, in which myelination in the peripheral nervous system is impaired. This opens promising perspectives for myelin chronic imaging in demyelinating diseases and for minimally invasive medical diagnosis.

Deep machine learning based Image classification in hard disk drive manufacturing (Conference Presentation)

Science.gov (United States)

Rana, Narender; Chien, Chester

2018-03-01

A key sensor element in a Hard Disk Drive (HDD) is the read-write head device. The device is complex 3D shape and its fabrication requires over thousand process steps with many of them being various types of image inspection and critical dimension (CD) metrology steps. In order to have high yield of devices across a wafer, very tight inspection and metrology specifications are implemented. Many images are collected on a wafer and inspected for various types of defects and in CD metrology the quality of image impacts the CD measurements. Metrology noise need to be minimized in CD metrology to get better estimate of the process related variations for implementing robust process controls. Though there are specialized tools available for defect inspection and review allowing classification and statistics. However, due to unavailability of such advanced tools or other reasons, many times images need to be manually inspected. SEM Image inspection and CD-SEM metrology tools are different tools differing in software as well. SEM Image inspection and CD-SEM metrology tools are separate tools differing in software and purpose. There have been cases where a significant numbers of CD-SEM images are blurred or have some artefact and there is a need for image inspection along with the CD measurement. Tool may not report a practical metric highlighting the quality of image. Not filtering CD from these blurred images will add metrology noise to the CD measurement. An image classifier can be helpful here for filtering such data. This paper presents the use of artificial intelligence in classifying the SEM images. Deep machine learning is used to train a neural network which is then used to classify the new images as blurred and not blurred. Figure 1 shows the image blur artefact and contingency table of classification results from the trained deep neural network. Prediction accuracy of 94.9 % was achieved in the first model. Paper covers other such applications of the deep neural

Micropropagation of the new apple rootstock ‘G. 814’

Directory of Open Access Journals (Sweden)

Aline Meneguzzi

Full Text Available ABSTRACT: International breeding programs launched new genetic material of apple rootstocks that in addition to precocity and great yield are resistant to major diseases and soil pests encountered in the largest apple producing regions in Brazil. Given this, there is a necessity for vegetative propagation of these materials for study and possible replacement of existing rootstocks. The objective was to adapt a micropropagation protocol for new apple rootstock ‘G. 814’. In the multiplication phase were evaluated BAP concentrations: 0; 0.5; 1; 2 and 4mg L-1 and in the rooting phase were evaluated IBA concentrations: 0; 0.25; 0.50; 1; 1.5 and 2.5mg L-1. These new results demonstrated that this new rootstock selection can be propagated with this tissue culture adapted protocol. For the successful in vitro propagation of apple rootstock ‘G. 814’ it is indicated the use of 1mg L-1 BAP at multiplication phase and 1.5mg L-1 IBA at rooting phase.

18F-FDG PET imaging on the neuronal network of Parkinson's disease patients following deep brain stimulation of bilateral subthalamic nucleus

International Nuclear Information System (INIS)

Zuo Chuantao; Huang Zhemin; Zhao Jun; Guan Yihui; Lin Xiangtong; Li Dianyou; Sun Bomin

2007-01-01

Objective: There is evidence that the cause and progression of Parkinson's disease (PD) may be attributed to subthalamic nucleus (STN) dysfunction and that external electrical stimulation of the STN may improve the underlying neuronal network. This study aimed at using 18 F-FDG PET to monitor the functional status of the neuronal network of advanced PD patients following deep brain stimulation (DBS) of bilateral STN. Methods: Five PD patients in advanced stage, rated according to unified PD rat- ing scale (UPDRS) motion score, underwent bilateral STN DBS implantation. Six months after the implantation, each patient was studied with 18 F-FDG PET scans under stimulation turned 'on' and 'off' conditions. Statistical parametric mapping 2 (SPM2) was applied for data analyses. Results: Bilateral STN DBS reduced glucose utilization in lentiform nucleus (globus pallidus), bilateral thalamus, cerebellum, as well as the distal parietal cortex. However, glucose utilization in midbrain and pons was increased. The PD-related pattern (PDRP) scores were significantly different during the 'on' status (2.12 ± 15.24) and 'off' status (4.93 ± 13.01), which corresponded to the clinical improvement of PD symptoms as PDRP scores decreased. Conclusion: 18 F-FDG PET may be useful in monitoring and mapping the metabolism of the neuronal network during bilateral STN DBS, thus supporting its therapeutic impact on PD patients. (authors)

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

Energy Technology Data Exchange (ETDEWEB)

Kang, Jiayin [School of Electronics Engineering, Huaihai Institute of Technology, Lianyungang, Jiangsu 222005, China and IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Feng [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Lalush, David S. [Joint UNC-NCSU Department of Biomedical Engineering, North Carolina State University, Raleigh, North Carolina 27695 (United States); Lin, Weili [MRI Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

2015-09-15

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET

Prediction of standard-dose brain PET image by using MRI and low-dose brain ["1"8F]FDG PET images

International Nuclear Information System (INIS)

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

2015-01-01

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain ["1"8F]FDG PET image by using a low-dose brain ["1"8F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain ["1"8F]FDG PET image by low-dose brain ["1"8F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain ["1"8F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain ["1"8F]FDG PET image and substantially

Variation of multiplicity and transverse energy flow with W2 and Q2 in deep inelastic scattering at HERA

International Nuclear Information System (INIS)

Lohmander, H.

1995-04-01

Charged particle and transverse energy flow for deep inelastic ep scattering at HERA have been investigated in the hadronic center of mass systems as a function of pseudorapidity η* in different W 2 and Q 2 intervals. In addition, the mean charged particle multiplicity ch > and the mean transverse energy * Τ > as a function of W 2 and Q 2 have been studied. The measurements were made in the kinematic region 85 2 2 . The ch > was found to increase with increasing W 2 at fixed Q 2 but did not show any significant dependence on Q 2 at fixed W 2 . The best description of the mean charged multiplicity is given by ch >=a+b·ln(W 2 /GeV 2 ) with a=-1.38±0.07 and b=0.93±0.05. The * Τ > increased both with increasing W 2 at fixed Q 2 and with increasing Q 2 at fixed W 2 . The mean transverse energy is described by * Τ >=a+b·ln(W 2 /GeV 2 )+c·ln (Q 2 /GeV 2 )GeV with a=-5.93±0.07, b=1.28±0.06 and c=0.69±0.02. Different QCD models have been compared with data. Only the Color Dipole Model, as implemented in the Monte Carlo program Ariadne, describes the data satisfactorily. 29 refs

Enhancing SDO/HMI images using deep learning

Science.gov (United States)

Baso, C. J. Díaz; Ramos, A. Asensio

2018-06-01

Context. The Helioseismic and Magnetic Imager (HMI) provides continuum images and magnetograms with a cadence better than one per minute. It has been continuously observing the Sun 24 h a day for the past 7 yr. The trade-off between full disk observations and spatial resolution means that HMI is not adequate for analyzing the smallest-scale events in the solar atmosphere. Aims: Our aim is to develop a new method to enhance HMI data, simultaneously deconvolving and super-resolving images and magnetograms. The resulting images will mimic observations with a diffraction-limited telescope twice the diameter of HMI. Methods: Our method, which we call Enhance, is based on two deep, fully convolutional neural networks that input patches of HMI observations and output deconvolved and super-resolved data. The neural networks are trained on synthetic data obtained from simulations of the emergence of solar active regions. Results: We have obtained deconvolved and super-resolved HMI images. To solve this ill-defined problem with infinite solutions we have used a neural network approach to add prior information from the simulations. We test Enhance against Hinode data that has been degraded to a 28 cm diameter telescope showing very good consistency. The code is open source.

Imaging Features of Superficial and Deep Fibromatoses in the Adult Population

Directory of Open Access Journals (Sweden)

Eric A. Walker

2012-01-01

Full Text Available The fibromatoses are a group of benign fibroblastic proliferations that vary from benign to intermediate in biological behavior. This article will discuss imaging characteristics and patient demographics of the adult type superficial (fascial and deep (musculoaponeurotic fibromatoses. The imaging appearance of these lesions can be characteristic (particularly when using magnetic resonance imaging. Palmar fibromatosis demonstrates multiple nodular or band-like soft tissue masses arising from the proximal palmar aponeurosis and extending along the subcutaneous tissues of the finger in parallel to the flexor tendons. T1 and T2-weighted signal intensity can vary from low (higher collagen to intermediate (higher cellularity, similar to the other fibromatoses. Plantar fibromatosis manifests as superficial lesions along the deep plantar aponeurosis, which typically blend with the adjacent plantar musculature. Linear tails of extension (“fascial tail sign” along the aponeurosis are frequent. Extraabdominal and abdominal wall fibromatosis often appear as a heterogeneous lesion with low signal intensity bands on all pulse sequences and linear fascial extensions (“fascial tail” sign with MR imaging. Mesenteric fibromatosis usually demonstrates a soft tissue density on CT with radiating strands projecting into the adjacent mesenteric fat. When imaging is combined with patient demographics, a diagnosis can frequently be obtained.

Robust Single Image Super-Resolution via Deep Networks With Sparse Prior.

Science.gov (United States)

Liu, Ding; Wang, Zhaowen; Wen, Bihan; Yang, Jianchao; Han, Wei; Huang, Thomas S

2016-07-01

Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-resolution image from its low-resolution observation. To regularize the solution of the problem, previous methods have focused on designing good priors for natural images, such as sparse representation, or directly learning the priors from a large data set with models, such as deep neural networks. In this paper, we argue that domain expertise from the conventional sparse coding model can be combined with the key ingredients of deep learning to achieve further improved results. We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data. The network has a cascaded structure, which boosts the SR performance for both fixed and incremental scaling factors. The proposed training and testing schemes can be extended for robust handling of images with additional degradation, such as noise and blurring. A subjective assessment is conducted and analyzed in order to thoroughly evaluate various SR techniques. Our proposed model is tested on a wide range of images, and it significantly outperforms the existing state-of-the-art methods for various scaling factors both quantitatively and perceptually.

Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging

Science.gov (United States)

Nguyen, Thinh; Potter, Thomas; Grossman, Robert; Zhang, Yingchun

2018-06-01

Objective. Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error. The dynamic brain transition network (DBTN) approach, a spatiotemporal fMRI constrained EEG source imaging method, has recently been developed to address these issues by solving the EEG inverse problem in a Bayesian framework, utilizing fMRI priors in a spatial and temporal variant manner. This paper presents a computer simulation study to provide a detailed characterization of the spatial and temporal accuracy of the DBTN method. Approach. Synthetic EEG data were generated in a series of computer simulations, designed to represent realistic and complex brain activity at superficial and deep sources with highly dynamical activity time-courses. The source reconstruction performance of the DBTN method was tested against the fMRI-constrained minimum norm estimates algorithm (fMRIMNE). The performances of the two inverse methods were evaluated both in terms of spatial and temporal accuracy. Main results. In comparison with the commonly used fMRIMNE method, results showed that the DBTN method produces results with increased spatial and temporal accuracy. The DBTN method also demonstrated the capability to reduce crosstalk in the reconstructed cortical time-course(s) induced by neighboring regions, mitigate depth bias and improve overall localization accuracy. Significance. The improved spatiotemporal accuracy of the reconstruction allows for an improved characterization of complex neural activity. This improvement can be

ULTRA-COMPACT DWARFS IN THE CORE OF THE COMA CLUSTER

International Nuclear Information System (INIS)

Madrid, Juan P.; Graham, Alister W.; Forbes, Duncan A.; Spitler, Lee R.; Harris, William E.; Goudfrooij, Paul; Ferguson, Henry C.; Carter, David; Blakeslee, John P.

2010-01-01

We have discovered both a red and a blue subpopulation of ultra-compact dwarf (UCD) galaxy candidates in the Coma galaxy cluster. We analyzed deep F475W (Sloan g) and F814W (I) Hubble Space Telescope images obtained with the Advanced Camera for Surveys Wide Field Channel as part of the Coma Cluster Treasury Survey and have fitted the light profiles of ∼5000 point-like sources in the vicinity of NGC 4874, one of the two central dominant galaxies of the Coma Cluster. Although almost all of these sources are globular clusters that remain unresolved, we found that 52 objects have effective radii between ∼10 and 66 pc, in the range spanned by dwarf globular transition objects (DGTOs) and UCDs. Of these 52 compact objects, 25 are brighter than M V ∼ -11 mag, a magnitude conventionally thought to separate UCDs and globular clusters. The UCD/DGTO candidates have the same color and luminosity distribution as the most luminous globular clusters within the red and blue subpopulations of the immensely rich NGC 4874 globular cluster system. Unlike standard globular clusters, blue and red UCD/DGTO subpopulations have the same median effective radius. The spatial distribution of UCD/DGTO candidates reveals that they congregate toward NGC 4874 and are not uniformly distributed. We find a relative deficit of UCD/DGTOs compared with globular clusters in the inner 15 kpc around NGC 4874; however, at larger radii UCD/DGTO and globular clusters follow the same spatial distribution.

STUDY ON THE CLASSIFICATION OF GAOFEN-3 POLARIMETRIC SAR IMAGES USING DEEP NEURAL NETWORK

Directory of Open Access Journals (Sweden)

J. Zhang

2018-04-01

Full Text Available Polarimetric Synthetic Aperture Radar（POLSAR） imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, the paper studied the types of the surface cover by using the method of Deep Learning. We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test the classification of data validation.First of all, referring to the optical image, we mark the surface coverage type of GF-3 POLSAR image with 8m resolution, and then collect the samples according to different categories. To meet the GoogLeNet model requirements of 256 × 256 pixel image input and taking into account the lack of full-resolution SAR resolution, the original image should be pre-processed in the process of resampling. In this paper, POLSAR image slice samples of different scales with sampling intervals of 2 m and 1 m to be trained separately and validated by the verification dataset. Among them, the training accuracy of GoogLeNet model trained with resampled 2-m polarimetric SAR image is 94.89 %, and that of the trained SAR image with resampled 1 m is 92.65 %.

Study on the Classification of GAOFEN-3 Polarimetric SAR Images Using Deep Neural Network

Science.gov (United States)

Zhang, J.; Zhang, J.; Zhao, Z.

2018-04-01

Polarimetric Synthetic Aperture Radar (POLSAR) imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, the paper studied the types of the surface cover by using the method of Deep Learning. We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test the classification of data validation.First of all, referring to the optical image, we mark the surface coverage type of GF-3 POLSAR image with 8m resolution, and then collect the samples according to different categories. To meet the GoogLeNet model requirements of 256 × 256 pixel image input and taking into account the lack of full-resolution SAR resolution, the original image should be pre-processed in the process of resampling. In this paper, POLSAR image slice samples of different scales with sampling intervals of 2 m and 1 m to be trained separately and validated by the verification dataset. Among them, the training accuracy of GoogLeNet model trained with resampled 2-m polarimetric SAR image is 94.89 %, and that of the trained SAR image with resampled 1 m is 92.65 %.

Topography characterization of a deep grating using near-field imaging

DEFF Research Database (Denmark)

Gregersen, Niels; Tromborg, Bjarne; Volkov, Valentyn S.

2006-01-01

Using near-field optical microscopy at the wavelength of 633 nm, we image light intensity distributions at several distances above an ~2-mm deep and a 1-mm-period glass grating illuminated from below under the condition of total internal reflection. The intensity distributions are numerically mod...

Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment.

Science.gov (United States)

Ithapu, Vamsi K; Singh, Vikas; Okonkwo, Ozioma C; Chappell, Richard J; Dowling, N Maritza; Johnson, Sterling C

2015-12-01

The mild cognitive impairment (MCI) stage of Alzheimer's disease (AD) may be optimal for clinical trials to test potential treatments for preventing or delaying decline to dementia. However, MCI is heterogeneous in that not all cases progress to dementia within the time frame of a trial and some may not have underlying AD pathology. Identifying those MCIs who are most likely to decline during a trial and thus most likely to benefit from treatment will improve trial efficiency and power to detect treatment effects. To this end, using multimodal, imaging-derived, inclusion criteria may be especially beneficial. Here, we present a novel multimodal imaging marker that predicts future cognitive and neural decline from [F-18]fluorodeoxyglucose positron emission tomography (PET), amyloid florbetapir PET, and structural magnetic resonance imaging, based on a new deep learning algorithm (randomized denoising autoencoder marker, rDAm). Using ADNI2 MCI data, we show that using rDAm as a trial enrichment criterion reduces the required sample estimates by at least five times compared with the no-enrichment regime and leads to smaller trials with high statistical power, compared with existing methods. Copyright © 2015 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Small Animal [18F]FDG PET Imaging for Tumor Model Study

International Nuclear Information System (INIS)

Woo, Sang Keun; Kim, Kyeong Min; Cheon, Gi Jeong

2008-01-01

PET allows non-invasive, quantitative and repetitive imaging of biological function in living animals. Small animal PET imaging with [ 18 F]FDG has been successfully applied to investigation of metabolism, receptor, ligand interactions, gene expression, adoptive cell therapy and somatic gene therapy. Experimental condition of animal handling impacts on the biodistribution of [ 18 F]FDG in small animal study. The small animal PET and CT images were registered using the hardware fiducial markers and small animal contour point. Tumor imaging in small animal with small animal [ 18 F]FDG PET should be considered fasting, warming, and isoflurane anesthesia level. Registered imaging with small animal PET and CT image could be useful for the detection of tumor. Small animal experimental condition of animal handling and registration method will be of most importance for small lesion detection of metastases tumor model

A Strong-Lens Survey in AEGIS: the influence of large scalestructure

Energy Technology Data Exchange (ETDEWEB)

Moustakas, Leonidas A.; Marshall, Phil; Newman, Jeffrey A.; Coil,Alison L.; Cooper, Michael C.; Davis, Marc; Fassnacht, Christopher D.; Guhathakurta, Puragra; Hopkins, Andrew; Koekemoer, Anton; Konidaris,Nicholas P.; Lotz, Jennifer M.; Willmer, Christopher N. A.

2006-10-13

We report on the results of a visual search for galaxy-scale strong gravitational lenses over 650 arcmin{sup 2} of HST/ACS (F606W and F814W) imaging in the DEEP2-Extended Groth Strip (EGS). In addition to a previously-known Einstein Cross also found by our search (the 'Cross', HSTJ141735+52264, z{sub lens} = 0.8106, z{sub source} = 3.40), we identify two new strong galaxy-galaxy lenses with multiple extended arcs. The first, HSTJ141820+52361 (the 'Dewdrop'; z{sub lens} = 0.5798), lenses two distinct extended sources into two pairs of arcs (z{sub source} = 0.9818), while the second, HSTJ141833+52435 (the 'Anchor'; z{sub lens} = 0.4625), produces a single pair of arcs (z{sub lens} not yet known). Four less convincing arc/counter-arc and two-image lens candidates are also found and presented for completeness. Lenses are found in a both underdense and overdense local environments, as characterized by a robust measure, 1+{delta}{sub 3}, a normalized density that uses the distance to the third nearest neighbor. All three definite lenses are fit reasonably well by simple singular isothermal ellipsoid models including external shear, giving {chi}{sub {nu}}{sup 2} values close to unity. These shears are much greater than those implied by a simple consideration of the three-dimensional convergence and shear from galaxies along the line of sight, where each galaxy is approximated by a singular isothermal sphere halo truncated at 200 h{sup -1} kpc. This shows how a realistic treatment of galaxies and the large scale structure they are embedded in is necessary, and that simply characterizing the very-local environment may be insufficient.

Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

Science.gov (United States)

Xu, Kele; Feng, Dawei; Mi, Haibo

2017-11-23

The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.

Synthesis, biological evaluation, and baboon PET imaging of the potential adrenal imaging agent cholesteryl-p-[18f]fluorobenzoate

International Nuclear Information System (INIS)

Jonson, Stephanie D.; Welch, Michael J.

1999-01-01

Cholesteryl-p-[ 18 F]fluorobenzoate ([ 18 F]CFB) was investigated as a potential adrenal positron emission tomography (PET) imaging agent for the diagnostic imaging of adrenal disorders. We describe the synthesis, biodistribution, adrenal autoradiography, and baboon PET imaging of [ 18 F]CFB. The synthesis of [ 18 F]CFB was facilitated by the use of a specially designed microwave cavity that was instrumental in effecting 70-83% incorporation of fluorine-18 in 60 s via [ 18 F]fluoro-for-nitro exchange. Tissue distribution studies in mature female Sprague-Dawley rats showed good accumulation of [ 18 F]CFB in the steroid-secreting tissues, adrenals and ovaries, at 1 h postinjection. The effectiveness of [ 18 F]CFB to accumulate in diseased adrenals was shown through biodistribution studies in hypolipidemic rats, which showed a greater than threefold increase in adrenal uptake at 1 h and increased adrenal/liver and adrenal/kidney ratios. Analysis of the metabolites at 1 h in the blood, adrenals, spleen, and ovaries of hypolipidemic and control rats showed the intact tracer representing greater than 86%, 93%, 92%, and 82% of the accumulated activity, respectively. [ 18 F]CFB was confirmed to selectively accumulate in the adrenal cortex versus the adrenal medulla by autoradiography. Normal baboon PET imaging with [ 18 F]CFB effectively showed adrenal localization as early as 15 min after injection of the tracer, with enhanced adrenal contrast seen at 60-70 min. These results suggest that [ 18 F]CFB may be useful as an adrenal PET imaging agent for assessing adrenal disorders

Deep convolutional networks for pancreas segmentation in CT imaging

Science.gov (United States)

Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

2015-03-01

Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

Applications of two-photon fluorescence microscopy in deep-tissue imaging

Science.gov (United States)

Dong, Chen-Yuan; Yu, Betty; Hsu, Lily L.; Kaplan, Peter D.; Blankschstein, D.; Langer, Robert; So, Peter T. C.

2000-07-01

Based on the non-linear excitation of fluorescence molecules, two-photon fluorescence microscopy has become a significant new tool for biological imaging. The point-like excitation characteristic of this technique enhances image quality by the virtual elimination of off-focal fluorescence. Furthermore, sample photodamage is greatly reduced because fluorescence excitation is limited to the focal region. For deep tissue imaging, two-photon microscopy has the additional benefit in the greatly improved imaging depth penetration. Since the near- infrared laser sources used in two-photon microscopy scatter less than their UV/glue-green counterparts, in-depth imaging of highly scattering specimen can be greatly improved. In this work, we will present data characterizing both the imaging characteristics (point-spread-functions) and tissue samples (skin) images using this novel technology. In particular, we will demonstrate how blind deconvolution can be used further improve two-photon image quality and how this technique can be used to study mechanisms of chemically-enhanced, transdermal drug delivery.

18F-FDOPA PET/CT imaging of insulinoma revisited

International Nuclear Information System (INIS)

Imperiale, Alessio; Namer, Izzie-Jacques; Sebag, Frederic; Vix, Michel; Castinetti, Frederic; Kessler, Laurence; Moreau, Francois; Bachellier, Philippe; Guillet, Benjamin; Mundler, Olivier; Taieb, David

2015-01-01

18 F-FDOPA PET imaging is increasingly used in the work-up of patients with neuroendocrine tumours. It has been shown to be of limited value in localizing pancreatic insulin-secreting tumours in adults with hyperinsulinaemic hypoglycaemia (HH) mainly due to 18 F-FDOPA uptake by the whole pancreatic gland. The objective of this study was to review our experience with 18 F-FDOPA PET/CT imaging with carbidopa (CD) premedication in patients with HH in comparison with PET/CT studies performed without CD premedication in an independent population. A retrospective study including 16 HH patients who were investigated between January 2011 and December 2013 using 18 F-FDOPA PET/CT (17 examinations) in two academic endocrine tumour centres was conducted. All PET/CT examinations were performed under CD premedication (200 mg orally, 1 - 2 h prior to tracer injection). The PET/CT acquisition protocol included an early acquisition (5 min after 18 F-FDOPA injection) centred over the upper abdomen and a delayed whole-body acquisition starting 20 - 30 min later. An independent series of eight consecutive patients with HH and investigated before 2011 were considered for comparison. All patients had a reference whole-body PET/CT scan performed about 1 h after 18 F-FDOPA injection. In all cases, PET/CT was performed without CD premedication. In the study group, 18 F-FDOPA PET/CT with CD premedication was positive in 8 out of 11 patients with histologically proven insulinoma (73 %). All 18 F-FDOPA PET/CT-avid insulinomas were detected on early images and 5 of 11 (45 %) on delayed ones. The tumour/normal pancreas uptake ratio was not significantly different between early and delayed acquisitions. Considering all patients with HH, including those without imaging evidence of disease, the detection rate of the primary lesions using CD-assisted 18 F-FDOPA PET/CT was 53 %, showing 9 insulinomas in 17 studies performed. In the control group (without CD premedication, eight patients), the final

The Role of 18F-FDG PET/CT Integrated Imaging in Distinguishing Malignant from Benign Pleural Effusion

Science.gov (United States)

Sun, Yajuan; Yu, Hongjuan; Ma, Jingquan

2016-01-01

Objective The aim of our study was to evaluate the role of 18F-FDG PET/CT integrated imaging in differentiating malignant from benign pleural effusion. Methods A total of 176 patients with pleural effusion who underwent 18F-FDG PET/CT examination to differentiate malignancy from benignancy were retrospectively researched. The images of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging were visually analyzed. The suspected malignant effusion was characterized by the presence of nodular or irregular pleural thickening on CT imaging. Whereas on PET imaging, pleural 18F-FDG uptake higher than mediastinal activity was interpreted as malignant effusion. Images of 18F-FDG PET/CT integrated imaging were interpreted by combining the morphologic feature of pleura on CT imaging with the degree and form of pleural 18F-FDG uptake on PET imaging. Results One hundred and eight patients had malignant effusion, including 86 with pleural metastasis and 22 with pleural mesothelioma, whereas 68 patients had benign effusion. The sensitivities of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging in detecting malignant effusion were 75.0%, 91.7% and 93.5%, respectively, which were 69.8%, 91.9% and 93.0% in distinguishing metastatic effusion. The sensitivity of 18F-FDG PET/CT integrated imaging in detecting malignant effusion was higher than that of CT imaging (p = 0.000). For metastatic effusion, 18F-FDG PET imaging had higher sensitivity (p = 0.000) and better diagnostic consistency with 18F-FDG PET/CT integrated imaging compared with CT imaging (Kappa = 0.917 and Kappa = 0.295, respectively). The specificities of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging were 94.1%, 63.2% and 92.6% in detecting benign effusion. The specificities of CT imaging and 18F-FDG PET/CT integrated imaging were higher than that of 18F-FDG PET imaging (p = 0.000 and p = 0.000, respectively), and CT imaging had better diagnostic consistency with

The Role of 18F-FDG PET/CT Integrated Imaging in Distinguishing Malignant from Benign Pleural Effusion.

Science.gov (United States)

Sun, Yajuan; Yu, Hongjuan; Ma, Jingquan; Lu, Peiou

2016-01-01

The aim of our study was to evaluate the role of 18F-FDG PET/CT integrated imaging in differentiating malignant from benign pleural effusion. A total of 176 patients with pleural effusion who underwent 18F-FDG PET/CT examination to differentiate malignancy from benignancy were retrospectively researched. The images of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging were visually analyzed. The suspected malignant effusion was characterized by the presence of nodular or irregular pleural thickening on CT imaging. Whereas on PET imaging, pleural 18F-FDG uptake higher than mediastinal activity was interpreted as malignant effusion. Images of 18F-FDG PET/CT integrated imaging were interpreted by combining the morphologic feature of pleura on CT imaging with the degree and form of pleural 18F-FDG uptake on PET imaging. One hundred and eight patients had malignant effusion, including 86 with pleural metastasis and 22 with pleural mesothelioma, whereas 68 patients had benign effusion. The sensitivities of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging in detecting malignant effusion were 75.0%, 91.7% and 93.5%, respectively, which were 69.8%, 91.9% and 93.0% in distinguishing metastatic effusion. The sensitivity of 18F-FDG PET/CT integrated imaging in detecting malignant effusion was higher than that of CT imaging (p = 0.000). For metastatic effusion, 18F-FDG PET imaging had higher sensitivity (p = 0.000) and better diagnostic consistency with 18F-FDG PET/CT integrated imaging compared with CT imaging (Kappa = 0.917 and Kappa = 0.295, respectively). The specificities of CT imaging, 18F-FDG PET imaging and 18F-FDG PET/CT integrated imaging were 94.1%, 63.2% and 92.6% in detecting benign effusion. The specificities of CT imaging and 18F-FDG PET/CT integrated imaging were higher than that of 18F-FDG PET imaging (p = 0.000 and p = 0.000, respectively), and CT imaging had better diagnostic consistency with 18F-FDG PET/CT integrated

Automated synthesis of the estrogen receptors imaging agent 18F-FES

International Nuclear Information System (INIS)

Guo Shen; Chen Guobao; Dai Hongfeng; Lin Meifu; Chen Wenxin

2011-01-01

Objective: 18 F-16α-17β-fluoroestradiol ( 18 F-FES), an estrogen receptors imaging agent, is synthesized with Tracerlab FX FN system. Methods: 18 F-FES is obtained by two steps reactions, including the nucleophilic displacement reaction of no-carrier-added 18 F-fluoride with 3-O-methoxymethyl-16, 17-O-sulfuryl-16-epiesteriol, then the intermediate is evaporated and hydrolyzed with HCI and finally gives 18 F-FES. Results: The synthesis of 18 F-FES can be completed in about 80 min.The radiochemical yield and radio-chemical purity are about 10% and 95% respectively. Conclusion: The procedure of synthesis is simple and automatical. 18 F-FES has an extremely low toxicity, which suggests that 18 F-FES may be a safe, a nd effective estrogen receptors imaging agent. (authors)

ToF-SIMS images and spectra of biomimetic calcium silicate-based cements after storage in solutions simulating the effects of human biological fluids

Science.gov (United States)

Torrisi, A.; Torrisi, V.; Tuccitto, N.; Gandolfi, M. G.; Prati, C.; Licciardello, A.

2010-01-01

ToF-SIMS images were obtained from a section of a tooth, obturated by means of a new calcium-silicate based cement (wTCF) after storage for 1 month in a saline solutions (DPBS), in order to simulate the body fluid effects on the obturation. Afterwards, ToF-SIMS spectra were obtained from model samples, prepared by using the same cement paste, after storage for 1 month and 8 months in two different saline solutions (DPBS and HBSS). ToF-SIMS spectra were also obtained from fluorine-free cement (wTC) samples after storage in HBSS for 1 month and 8 months and used for comparison. It was found that the composition of both the saline solution and the cement influenced the composition of the surface of disks and that longer is the storage greater are the differences. Segregation phenomena occur both on the cement obturation of the tooth and on the surface of the disks prepared by using the same cement. Indirect evidences of formation of new crystalline phases are supplied.

F-18 FDG PET/CT imaging of primary hepatic neuroendocrine tumor

Directory of Open Access Journals (Sweden)

Katsuya Mitamura

2015-01-01

Full Text Available Primary hepatic neuroendocrine tumors (PHNETs are extremely rare neoplasms. Herein, we report a case of a 70-year-old man with a hepatic mass. The non-contrast computed tomography (CT image showed a low-density mass, and dynamic CT images indicated the enhancement of the mass in the arterial phase and early washout in the late phase. F18- fluorodeoxyglucose (18F-FDG positron emission tomography (PET and fused PET/CT images showed increased uptake in the hepatic mass. Whole-body 18F-FDG PET images showed no abnormal activity except for the liver lesion. Presence of an extrahepatic tumor was also ruled out by performing upper gastrointestinal endoscopy, total colonoscopy, and chest and abdominal CT. A posterior segmentectomy was performed, and histologic examination confirmed a neuroendocrine tumor (grade 1. The patient was followed up for about 2 years after the resection, and no extrahepatic lesions were radiologically found. Therefore, the patient was diagnosed with PHNET. To the best of our knowledge, no previous case of PHNET have been detected by 18F-FDG PET imaging.

Design of a Novel W-Sinker RF LDMOS

Directory of Open Access Journals (Sweden)

Xiangming Xu

2015-01-01

Full Text Available A novel RF LDMOS device structure and corresponding manufacturing process are presented in this paper. Deep trench W-sinker (tungsten sinker is employed in this technology to replace the traditional heavily doped diffusion sinker which can shrink chip size of the LDMOS transistor by more than 30% and improve power density. Furthermore, the W-sinker structure reduces the parasitic resistance and inductance and improves thermal conductivity of the device as well. Combined with the adoption of the techniques, like grounded shield, step gate oxide, LDD optimization, and so forth, an advanced technology for RF LDMOS based on conventional 0.35 μm CMOS technology is well established. An F+A power amplifier product with frequency range of 1.8–2.1 GHz is developed for the application of 4G LTE base station and industry leading performance is achieved. The qualification results show that the device reliability and ruggedness can also meet requirement of the application.

S-band low noise amplifier and 40 kW high power amplifier subsystems of Japanese Deep Space Earth Station

Science.gov (United States)

Honma, K.; Handa, K.; Akinaga, W.; Doi, M.; Matsuzaki, O.

This paper describes the design and the performance of the S-band low noise amplifier and the S-band high power amplifier that have been developed for the Usuda Deep Space Station of the Institute of Space and Astronautical Science (ISAS), Japan. The S-band low noise amplifier consists of a helium gas-cooled parametric amplifier followed by three-stage FET amplifiers and has a noise temperature of 8 K. The high power amplifier is composed of two 28 kW klystrons, capable of transmitting 40 kW continuously when two klystrons are combined. Both subsystems are operating quite satisfactorily in the tracking of Sakigake and Suisei, the Japanese interplanetary probes for Halley's comet exploration, launched by ISAS in 1985.

Deep Joint Rain Detection and Removal from a Single Image

OpenAIRE

Yang, Wenhan; Tan, Robby T.; Feng, Jiashi; Liu, Jiaying; Guo, Zongming; Yan, Shuicheng

2016-01-01

In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in the new rain image models and a novel deep learning architecture. We first modify an existing model comprising a rain streak layer and a background layer, by adding a binary map that locates rain streak regions. Second, we create a new model consisting of a component representing rain streak accumulation (where individual streaks cannot b...

f ( λ , μ $f_{(\\lambda,\\mu}$ -statistical convergence of order α̃ for double sequences

Directory of Open Access Journals (Sweden)

Mahmut Işik

2017-10-01

Full Text Available Abstract New concepts of f λ , μ $f_{\\lambda,\\mu }$ -statistical convergence for double sequences of order α̃ and strong f λ , μ $f_{\\lambda,\\mu }$ -Cesàro summability for double sequences of order α̃ are introduced for sequences of (complex or real numbers. Furthermore, we give the relationship between the spaces w α ˜ , 0 2 ( f , λ , μ $w_{\\tilde{\\alpha },0}^{2} ( f,\\lambda,\\mu $ , w α ˜ 2 ( f , λ , μ $w_{\\tilde{\\alpha }}^{2} ( f,\\lambda,\\mu $ and w α ˜ , ∞ 2 ( f , λ , μ $w_{\\tilde{\\alpha},\\infty }^{2} ( f,\\lambda,\\mu $ . Then we express the properties of strong f λ , μ $f_{\\lambda,\\mu }$ -Cesàro summability of order β̃ which is related to strong f λ , μ $f_{\\lambda,\\mu }$ -Cesàro summability of order α̃. Also, some relations between f λ , μ $f_{\\lambda,\\mu }$ -statistical convergence of order α̃ and strong f λ , μ $f_{\\lambda,\\mu }$ -Cesàro summability of order α̃ are given.

New 8,12;8,20-diepoxy-8,14-secopregnane hexa- and hepta-glycosides from the roots of Asclepias tuberosa.

Science.gov (United States)

Warashina, Tsutomu; Miyase, Toshio

2018-01-01

Previously, phytochemical investigation of the roots of Asclepias tuberosa (Asclepiadaceae) led to the isolation of some 8,12;8,20-diepoxy-8,14-secopregnane tri-, tetra-, and penta-glycosides. An additional eight new minor 8,12;8,20-diepoxy-8,14-secopregnane glycosides were afforded in the recent investigation of this plant. These glycosides consisted of six or seven 2,6-dideoxy-hexopyranoses together with the aglycone, tuberogenin. The structures of each of these compounds were established using NMR, mass spectroscopic analysis and chemical evidence. As 8,12;8,20-diepoxy-8,14-secopregnane-type glycosides were observed only in A. tuberosa, these compounds were considered to be characteristic phytochemicals of this plant.

Deep supervised dictionary learning for no-reference image quality assessment

Science.gov (United States)

Huang, Yuge; Liu, Xuesong; Tian, Xiang; Zhou, Fan; Chen, Yaowu; Jiang, Rongxin

2018-03-01

We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

Science.gov (United States)

Van Valen, David A; Kudo, Takamasa; Lane, Keara M; Macklin, Derek N; Quach, Nicolas T; DeFelice, Mialy M; Maayan, Inbal; Tanouchi, Yu; Ashley, Euan A; Covert, Markus W

2016-11-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Super-resolution for asymmetric resolution of FIB-SEM 3D imaging using AI with deep learning.

Science.gov (United States)

Hagita, Katsumi; Higuchi, Takeshi; Jinnai, Hiroshi

2018-04-12

Scanning electron microscopy equipped with a focused ion beam (FIB-SEM) is a promising three-dimensional (3D) imaging technique for nano- and meso-scale morphologies. In FIB-SEM, the specimen surface is stripped by an ion beam and imaged by an SEM installed orthogonally to the FIB. The lateral resolution is governed by the SEM, while the depth resolution, i.e., the FIB milling direction, is determined by the thickness of the stripped thin layer. In most cases, the lateral resolution is superior to the depth resolution; hence, asymmetric resolution is generated in the 3D image. Here, we propose a new approach based on an image-processing or deep-learning-based method for super-resolution of 3D images with such asymmetric resolution, so as to restore the depth resolution to achieve symmetric resolution. The deep-learning-based method learns from high-resolution sub-images obtained via SEM and recovers low-resolution sub-images parallel to the FIB milling direction. The 3D morphologies of polymeric nano-composites are used as test images, which are subjected to the deep-learning-based method as well as conventional methods. We find that the former yields superior restoration, particularly as the asymmetric resolution is increased. Our super-resolution approach for images having asymmetric resolution enables observation time reduction.

Visualizing deep neural network by alternately image blurring and deblurring.

Science.gov (United States)

Wang, Feng; Liu, Haijun; Cheng, Jian

2018-01-01

Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead to unrecognizable images, which cannot provide any meaningful information. In this paper, we introduce a simple but effective technique to constrain the optimization route of the visualization. By adding two totally inverse transformations, image blurring and deblurring, to the optimization procedure, recognizable images can be created. Our algorithm is good at extracting the details in the images, which are usually filtered by previous methods in the visualizations. Extensive experiments on AlexNet, VGGNet and GoogLeNet illustrate that we can better understand the neural networks utilizing the knowledge obtained by the visualization. Copyright © 2017 Elsevier Ltd. All rights reserved.

The evaluation and calculation of production cross sections of {sup 18}F, {sup 77}Br and {sup 186}Re medical radioisotopes from {sup 18}O, {sup 77}Se and {sup 186}W(p,n) reactions up to 80 MeV

Energy Technology Data Exchange (ETDEWEB)

Youxiang, Zhuang [Chinese Nuclear Data Center, Beijing, BJ (China)

1996-06-01

The medical radioisotopes are used for diagnostic and therapeutic purposes, as well as metabolism and physiological function researches in modern medicine. The major applications are of functional imaging using PET agents for {sup 18}F, various therapeutic pharmaceuticals via auger electrons for {sup 77}Br, cancers diagnosis and therapy for {sup 186}Re. The evaluations of experimental data and theoretical calculations for {sup 18}O, {sup 77}Se, {sup 187}W(p,n) reactions up to 80 MeV are presented and analysed. The recommended values for {sup 18}O(p,n){sup 18}F, {sup 77}Se(p,n){sup 77}Br and {sup 186}W(p,n){sup 186}Re reaction cross sections are given. (6 figs.).

Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues

Science.gov (United States)

Tzoumas, Stratis; Nunes, Antonio; Olefir, Ivan; Stangl, Stefan; Symvoulidis, Panagiotis; Glasl, Sarah; Bayer, Christine; Multhoff, Gabriele; Ntziachristos, Vasilis

2016-06-01

Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation, an effect that causes spectral corruption. Spectral corruption has limited the quantification accuracy of optical and optoacoustic spectroscopic methods, and impeded the goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical goal for the assessment of oxygenation in physiological processes and disease. Here we describe light fluence in the spectral domain and introduce eigenspectra multispectral optoacoustic tomography (eMSOT) to account for wavelength-dependent light attenuation, and estimate blood sO2 within deep tissue. We validate eMSOT in simulations, phantoms and animal measurements and spatially resolve sO2 in muscle and tumours, validating our measurements with histology data. eMSOT shows substantial sO2 accuracy enhancement over previous optoacoustic methods, potentially serving as a valuable tool for imaging tissue pathophysiology.

Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues.

Science.gov (United States)

Tzoumas, Stratis; Nunes, Antonio; Olefir, Ivan; Stangl, Stefan; Symvoulidis, Panagiotis; Glasl, Sarah; Bayer, Christine; Multhoff, Gabriele; Ntziachristos, Vasilis

2016-06-30

Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation, an effect that causes spectral corruption. Spectral corruption has limited the quantification accuracy of optical and optoacoustic spectroscopic methods, and impeded the goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical goal for the assessment of oxygenation in physiological processes and disease. Here we describe light fluence in the spectral domain and introduce eigenspectra multispectral optoacoustic tomography (eMSOT) to account for wavelength-dependent light attenuation, and estimate blood sO2 within deep tissue. We validate eMSOT in simulations, phantoms and animal measurements and spatially resolve sO2 in muscle and tumours, validating our measurements with histology data. eMSOT shows substantial sO2 accuracy enhancement over previous optoacoustic methods, potentially serving as a valuable tool for imaging tissue pathophysiology.

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

Science.gov (United States)

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

Fusion of shallow and deep features for classification of high-resolution remote sensing images

Science.gov (United States)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

TLR4 endogenous ligand MRP8/14 level in enthesitis-related arthritis and its association with disease activity and TLR4 expression.

Science.gov (United States)

Rahman, Mujeeb T; Myles, Arpita; Gaur, Priyanka; Misra, Ramnath; Aggarwal, Amita

2014-02-01

Enthesitis-related arthritis (ERA) is an inflammatory disease of childhood that lacks autoantibodies. Overexpression of surface-expressed Toll-like receptors (TLRs) has been found in ERA. Myeloid-related proteins (MRPs) 8 and 14 are calcium binding proteins that act as an endogenous ligand of TLR4. MRP8/14 levels are elevated in patients with systemic-onset arthritis. Thus we studied the role of MRP8/14 in ERA. The study enrolled patients with ERA. Plasma and SF levels of MRP8/14 were measured by ELISA and TLR4 expression on peripheral blood and SF monocytes was measured by two-colour flow cytometry. Control plasma samples were collected from 48 blood bank donors. Of the 69 patients, 67 were male, with a mean age of 15.2 (s.d. 2.7) years and a disease duration of 5 (s.d. 3) years. Median plasma levels of MRP8/14 were higher in patients (10 862.3 ng/ml) than controls (4426.1 ng/ml, P < 0.0001). Patients with active disease (11 669.5 ng/ml) had higher levels as compared with inactive disease (4421.8 ng/ml, P < 0.0001). Plasma MRP8/14 levels decreased on follow-up after 3 months only in patients who responded to treatment (P = 0.012). MRP8/14 levels were negatively correlated with the frequency of CD14(+)TLR4(+) cells (r = -0.372, P = 0.02). MRP8/14 levels were higher in SF as compared with plasma (15 858.45 ng/ml, P = 0.024). The frequency of CD14(+)TLR4(+) cells was higher in SF as compared with peripheral blood. MRP8/14 levels are increased in the plasma of ERA patients and are higher in those with active disease and the levels decrease in patients who respond to treatment, suggesting that it may be a good biomarker during follow-up.

18F-NaF PET/CT: EANM procedure guidelines for bone imaging

International Nuclear Information System (INIS)

Beheshti, M.; Langsteger, W.; Mottaghy, F.M.; Payche, F.; Behrendt, F.F.F.; Wyngaert, T.V. den; Fogelman, I.; Strobel, K.; Celli, M.; Fanti, S.; Giammarile, F.; Krause, B.

2015-01-01

The aim of this guideline is to provide minimum standards for the performance and interpretation of 18 F-NaF PET/CT scans. Standard acquisition and interpretation of nuclear imaging modalities will help to provide consistent data acquisition and numeric values between different platforms and institutes and to promote the use of PET/CT modality as an established diagnostic modality in routine clinical practice. This will also improve the value of scientific work and its contribution to evidence-based medicine. (orig.)

Normalization of cortical thickness measurements across different T1 magnetic resonance imaging protocols by novel W-Score standardization.

Science.gov (United States)

Chung, Jinyong; Yoo, Kwangsun; Lee, Peter; Kim, Chan Mi; Roh, Jee Hoon; Park, Ji Eun; Kim, Sang Joon; Seo, Sang Won; Shin, Jeong-Hyeon; Seong, Joon-Kyung; Jeong, Yong

2017-10-01

The use of different 3D T1-weighted magnetic resonance (T1 MR) imaging protocols induces image incompatibility across multicenter studies, negating the many advantages of multicenter studies. A few methods have been developed to address this problem, but significant image incompatibility still remains. Thus, we developed a novel and convenient method to improve image compatibility. W-score standardization creates quality reference values by using a healthy group to obtain normalized disease values. We developed a protocol-specific w-score standardization to control the protocol effect, which is applied to each protocol separately. We used three data sets. In dataset 1, brain T1 MR images of normal controls (NC) and patients with Alzheimer's disease (AD) from two centers, acquired with different T1 MR protocols, were used (Protocol 1 and 2, n = 45/group). In dataset 2, data from six subjects, who underwent MRI with two different protocols (Protocol 1 and 2), were used with different repetition times, echo times, and slice thicknesses. In dataset 3, T1 MR images from a large number of healthy normal controls (Protocol 1: n = 148, Protocol 2: n = 343) were collected for w-score standardization. The protocol effect and disease effect on subjects' cortical thickness were analyzed before and after the application of protocol-specific w-score standardization. As expected, different protocols resulted in differing cortical thickness measurements in both NC and AD subjects. Different measurements were obtained for the same subject when imaged with different protocols. Multivariate pattern difference between measurements was observed between the protocols. Classification accuracy between two protocols was nearly 90%. After applying protocol-specific w-score standardization, the differences between the protocols substantially decreased. Most importantly, protocol-specific w-score standardization reduced both univariate and multivariate differences in the images while

18F-FPYBF-2, a new F-18 labelled amyloid imaging PET tracer: biodistribution and radiation dosimetry assessment of first-in-man 18F-FPYBF-2 PET imaging.

Science.gov (United States)

Nishii, Ryuichi; Higashi, Tatsuya; Kagawa, Shinya; Okuyama, Chio; Kishibe, Yoshihiko; Takahashi, Masaaki; Okina, Tomoko; Suzuki, Norio; Hasegawa, Hiroshi; Nagahama, Yasuhiro; Ishizu, Koichi; Oishi, Naoya; Kimura, Hiroyuki; Watanabe, Hiroyuki; Ono, Masahiro; Saji, Hideo; Yamauchi, Hiroshi

2018-05-01

Recently, a benzofuran derivative for the imaging of β-amyloid plaques, 5-(5-(2-(2-(2- 18 F-fluoroethoxy)ethoxy)ethoxy)benzofuran-2-yl)- N-methylpyridin-2-amine ( 18 F-FPYBF-2) has been validated as a tracer for amyloid imaging and it was found that 18 F-FPYBF-2 PET/CT is a useful and reliable diagnostic tool for the evaluation of AD (Higashi et al. Ann Nucl Med, https://doi.org/10.1007/s12149-018-1236-1 , 2018). The aim of this study was to assess the biodistribution and radiation dosimetry of diagnostic dosages of 18 F-FPYBF-2 in normal healthy volunteers as a first-in-man study. Four normal healthy volunteers (male: 3, female: 1; mean age: 40 ± 17; age range 25-56) were included and underwent 18 F-FPYBF-2 PET/CT study for the evaluation of radiation exposure and pharmacokinetics. A 10-min dynamic PET/CT scan of the body (chest and abdomen) was performed at 0-10 min and a 15-min whole-body static scan was performed six times after the injection of 18 F-FPYBF-2. After reconstructing PET and CT image data, individual organ time-activity curves were estimated by fitting volume of interest data from the dynamic scan and whole-body scans. The OLINDA/EXM version 2.0 software was used to determine the whole-body effective doses. Dynamic PET imaging demonstrated that the hepatobiliary and renal systems were the principal pathways of clearance of 18 F-FPYBF-2. High uptake in the liver and the gall bladder, the stomach, and the kidneys were demonstrated, followed by the intestines and the urinary bladder. The ED for the adult dosimetric model was estimated to be 8.48 ± 1.25 µSv/MBq. The higher absorbed doses were estimated for the liver (28.98 ± 12.49 and 36.21 ± 15.64 µGy/MBq), the brain (20.93 ± 4.56 and 23.05 ± 5.03µ Gy/MBq), the osteogenic cells (9.67 ± 1.67 and 10.29 ± 1.70 µGy/MBq), the small intestines (9.12 ± 2.61 and 11.12 ± 3.15 µGy/MBq), and the kidneys (7.81 ± 2.62 and 8.71 ± 2.90 µGy/MBq) for

Using oral 18F-FDG for infection imaging

International Nuclear Information System (INIS)

Bolwell, Jacob J.

2009-01-01

Full text:A 22-year-old female with a complex medical history presented to our department with a complaint of pain around the site her Portocath (PaC). Multiple imaging techniques failed to identify any sign of infection around the pac. A 99 m Tc-Phytate Colloid labelled white cell (LWC) scan was arranged to identify any infective processes in or around the pac. Severe difficulty was encountered attempting to gain IV access aside from the pac and the LWC scan had to aborted. In order to identify infection of the pac a Positron Emission Tomography (PET) scan using oral administration 18F-fluorodeoxyglucose (18F-FDG) was arranged. The oral 18F-FDG PET scan showed active glucose metabolism around the site of the pac port and along the cathe tubing near the medial right clavicle. As a result of this the pac was removed and replaced and the patient is now receiving continued antibiotics and medication through her new POC. In conclusion we found oral administration of 18F-FDG to be a suitable alternative to IV administered 18F-FDG in on to obtain functional imaging in a case where there was severe difficulty in obtaining venous access.

25 CFR 166.814 - How will the BIA determine the value of the products or property illegally used or removed?

Science.gov (United States)

2010-04-01

... the BIA determine the value of the products or property illegally used or removed? We will determine the value of the products or property illegally used or removed based upon a valuation of similar... property illegally used or removed? 166.814 Section 166.814 Indians BUREAU OF INDIAN AFFAIRS, DEPARTMENT OF...

Deep brain two-photon NIR fluorescence imaging for study of Alzheimer's disease

Science.gov (United States)

Chen, Congping; Liang, Zhuoyi; Zhou, Biao; Ip, Nancy Y.; Qu, Jianan Y.

2018-02-01

Amyloid depositions in the brain represent the characteristic hallmarks of Alzheimer's disease (AD) pathology. The abnormal accumulation of extracellular amyloid-beta (Aβ) and resulting toxic amyloid plaques are considered to be responsible for the clinical deficits including cognitive decline and memory loss. In vivo two-photon fluorescence imaging of amyloid plaques in live AD mouse model through a chronic imaging window (thinned skull or craniotomy) provides a mean to greatly facilitate the study of the pathological mechanism of AD owing to its high spatial resolution and long-term continuous monitoring. However, the imaging depth for amyloid plaques is largely limited to upper cortical layers due to the short-wavelength fluorescence emission of commonly used amyloid probes. In this work, we reported that CRANAD-3, a near-infrared (NIR) probe for amyloid species with excitation wavelength at 900 nm and emission wavelength around 650 nm, has great advantages over conventionally used probes and is well suited for twophoton deep imaging of amyloid plaques in AD mouse brain. Compared with a commonly used MeO-X04 probe, the imaging depth of CRANAD-3 is largely extended for open skull cranial window. Furthermore, by using two-photon excited fluorescence spectroscopic imaging, we characterized the intrinsic fluorescence of the "aging pigment" lipofuscin in vivo, which has distinct spectra from CRANAD-3 labeled plaques. This study reveals the unique potential of NIR probes for in vivo, high-resolution and deep imaging of brain amyloid in Alzheimer's disease.

[Microsurgery assisted by intraoperative magnetic resonance imaging and neuronavigation for small lesions in deep brain].

Science.gov (United States)

Song, Zhi-jun; Chen, Xiao-lei; Xu, Bai-nan; Sun, Zheng-hui; Sun, Guo-chen; Zhao, Yan; Wang, Fei; Wang, Yu-bo; Zhou, Ding-biao

2012-01-03

To explore the practicability of resecting small lesions in deep brain by intraoperative magnetic resonance imaging (iMRI) and neuronavigator-assisted microsurgery and its clinical efficacies. A total of 42 cases with small lesions in deep brain underwent intraoperative MRI and neuronavigator-assisted microsurgery. The drifting of neuronavigation was corrected by images acquired from intraoperative MR rescanning. All lesions were successfully identified and 40 cases totally removed without mortality. Only 3 cases developed new neurological deficits post-operatively while 2 of them returned to normal neurological functions after a follow-up duration of 3 months to 2 years. The application of intraoperative MRI can effectively correct the drifting of neuronavigation and enhance the accuracy of microsurgical neuronavigation for small lesions in deep brain.

Synthesis, biological evaluation, and baboon PET imaging of the potential adrenal imaging agent cholesteryl-p-[{sup 18}f]fluorobenzoate

Energy Technology Data Exchange (ETDEWEB)

Jonson, Stephanie D.; Welch, Michael J. E-mail: welch@mirlink.wustl.edu

1999-01-01

Cholesteryl-p-[{sup 18}F]fluorobenzoate ([{sup 18}F]CFB) was investigated as a potential adrenal positron emission tomography (PET) imaging agent for the diagnostic imaging of adrenal disorders. We describe the synthesis, biodistribution, adrenal autoradiography, and baboon PET imaging of [{sup 18}F]CFB. The synthesis of [{sup 18}F]CFB was facilitated by the use of a specially designed microwave cavity that was instrumental in effecting 70-83% incorporation of fluorine-18 in 60 s via [{sup 18}F]fluoro-for-nitro exchange. Tissue distribution studies in mature female Sprague-Dawley rats showed good accumulation of [{sup 18}F]CFB in the steroid-secreting tissues, adrenals and ovaries, at 1 h postinjection. The effectiveness of [{sup 18}F]CFB to accumulate in diseased adrenals was shown through biodistribution studies in hypolipidemic rats, which showed a greater than threefold increase in adrenal uptake at 1 h and increased adrenal/liver and adrenal/kidney ratios. Analysis of the metabolites at 1 h in the blood, adrenals, spleen, and ovaries of hypolipidemic and control rats showed the intact tracer representing greater than 86%, 93%, 92%, and 82% of the accumulated activity, respectively. [{sup 18}F]CFB was confirmed to selectively accumulate in the adrenal cortex versus the adrenal medulla by autoradiography. Normal baboon PET imaging with [{sup 18}F]CFB effectively showed adrenal localization as early as 15 min after injection of the tracer, with enhanced adrenal contrast seen at 60-70 min. These results suggest that [{sup 18}F]CFB may be useful as an adrenal PET imaging agent for assessing adrenal disorders.

Measurement of the polarization of W bosons with large transverse momenta in W + jets events at the LHC.

Science.gov (United States)

Chatrchyan, S; Khachatryan, V; Sirunyan, A M; Tumasyan, A; Adam, W; Bergauer, T; Dragicevic, M; Erö, J; Fabjan, C; Friedl, M; Frühwirth, R; Ghete, V M; Hammer, J; Hänsel, S; Hoch, M; Hörmann, N; Hrubec, J; Jeitler, M; Kiesenhofer, W; Krammer, M; Liko, D; Mikulec, I; Pernicka, M; Rohringer, H; Schöfbeck, R; Strauss, J; Taurok, A; Teischinger, F; Wagner, P; Waltenberger, W; Walzel, G; Widl, E; Wulz, C-E; Mossolov, V; Shumeiko, N; Suarez Gonzalez, J; Benucci, L; De Wolf, E A; Janssen, X; Maes, J; Maes, T; Mucibello, L; Ochesanu, S; Roland, B; Rougny, R; Selvaggi, M; Van Haevermaet, H; Van Mechelen, P; Van Remortel, N; Blekman, F; Blyweert, S; D'Hondt, J; Devroede, O; Gonzalez Suarez, R; Kalogeropoulos, A; Maes, M; Van Doninck, W; Van Mulders, P; Van Onsem, G P; Villella, I; Charaf, O; Clerbaux, B; De Lentdecker, G; Dero, V; Gay, A P R; Hammad, G H; Hreus, T; Marage, P E; Thomas, L; Vander Velde, C; Vanlaer, P; Adler, V; Cimmino, A; Costantini, S; Grunewald, M; Klein, B; Lellouch, J; Marinov, A; Mccartin, J; Ryckbosch, D; Thyssen, F; Tytgat, M; Vanelderen, L; Verwilligen, P; Walsh, S; Zaganidis, N; Basegmez, S; Bruno, G; Caudron, J; Ceard, L; Cortina Gil, E; De Favereau De Jeneret, J; Delaere, C; Favart, D; Giammanco, A; Grégoire, G; Hollar, J; Lemaitre, V; Liao, J; Militaru, O; Ovyn, S; Pagano, D; Pin, A; Piotrzkowski, K; Schul, N; Beliy, N; Caebergs, T; Daubie, E; Alves, G A; De Jesus Damiao, D; Pol, M E; Souza, M H G; Carvalho, W; Da Costa, E M; De Oliveira Martins, C; Fonseca De Souza, S; Mundim, L; Nogima, H; Oguri, V; Prado Da Silva, W L; Santoro, A; Silva Do Amaral, S M; Sznajder, A; Torres Da Silva De Araujo, F; Dias, F A; Fernandez Perez Tomei, T R; Gregores, E M; Lagana, C; Marinho, F; Mercadante, P G; Novaes, S F; Padula, Sandra S; Darmenov, N; Dimitrov, L; Genchev, V; Iaydjiev, P; Piperov, S; Rodozov, M; Stoykova, S; Sultanov, G; Tcholakov, V; Trayanov, R; Vankov, I; Dimitrov, A; Hadjiiska, R; Karadzhinova, A; Kozhuharov, V; Litov, L; Mateev, M; Pavlov, B; Petkov, P; Bian, J G; Chen, G M; Chen, H S; Jiang, C H; Liang, D; Liang, S; Meng, X; Tao, J; Wang, J; Wang, J; Wang, X; Wang, Z; Xiao, H; Xu, M; Zang, J; Zhang, Z; Ban, Y; Guo, S; Guo, Y; Li, W; Mao, Y; Qian, S J; Teng, H; Zhang, L; Zhu, B; Zou, W; Cabrera, A; Gomez Moreno, B; Ocampo Rios, A A; Osorio Oliveros, A F; Sanabria, J C; Godinovic, N; Lelas, D; Lelas, K; Plestina, R; Polic, D; Puljak, I; Antunovic, Z; Dzelalija, M; Brigljevic, V; Duric, S; Kadija, K; Morovic, S; Attikis, A; Galanti, M; Mousa, J; Nicolaou, C; Ptochos, F; Razis, P A; Finger, M; Finger, M; Assran, Y; Khalil, S; Mahmoud, M A; Hektor, A; Kadastik, M; Müntel, M; Raidal, M; Rebane, L; Azzolini, V; Eerola, P; Fedi, G; Czellar, S; Härkönen, J; Heikkinen, A; Karimäki, V; Kinnunen, R; Kortelainen, M J; Lampén, T; Lassila-Perini, K; Lehti, S; Lindén, T; Luukka, P; Mäenpää, T; Tuominen, E; Tuominiemi, J; Tuovinen, E; Ungaro, D; Wendland, L; Banzuzi, K; Korpela, A; Tuuva, T; Sillou, D; Besancon, M; Choudhury, S; Dejardin, M; Denegri, D; Fabbro, B; Faure, J L; Ferri, F; Ganjour, S; Gentit, F X; Givernaud, A; Gras, P; Hamel de Monchenault, G; Jarry, P; Locci, E; Malcles, J; Marionneau, M; Millischer, L; Rander, J; Rosowsky, A; Shreyber, I; Titov, M; Verrecchia, P; Baffioni, S; Beaudette, F; Benhabib, L; Bianchini, L; Bluj, M; Broutin, C; Busson, P; Charlot, C; Dahms, T; Dobrzynski, L; Elgammal, S; Granier de Cassagnac, R; Haguenauer, M; Miné, P; Mironov, C; Ochando, C; Paganini, P; Sabes, D; Salerno, R; Sirois, Y; Thiebaux, C; Wyslouch, B; Zabi, A; Agram, J-L; Andrea, J; Bloch, D; Bodin, D; Brom, J-M; Cardaci, M; Chabert, E C; Collard, C; Conte, E; Drouhin, F; Ferro, C; Fontaine, J-C; Gelé, D; Goerlach, U; Greder, S; Juillot, P; Karim, M; Le Bihan, A-C; Mikami, Y; Van Hove, P; Fassi, F; Mercier, D; Baty, C; Beauceron, S; Beaupere, N; Bedjidian, M; Bondu, O; Boudoul, G; Boumediene, D; Brun, H; Chasserat, J; Chierici, R; Contardo, D; Depasse, P; El Mamouni, H; Fay, J; Gascon, S; Ille, B; Kurca, T; Le Grand, T; Lethuillier, M; Mirabito, L; Perries, S; Sordini, V; Tosi, S; Tschudi, Y; Verdier, P; Lomidze, D; Anagnostou, G; Edelhoff, M; Feld, L; Heracleous, N; Hindrichs, O; Jussen, R; Klein, K; Merz, J; Mohr, N; Ostapchuk, A; Perieanu, A; Raupach, F; Sammet, J; Schael, S; Sprenger, D; Weber, H; Weber, M; Wittmer, B; Ata, M; Bender, W; Dietz-Laursonn, E; Erdmann, M; Frangenheim, J; Hebbeker, T; Hinzmann, A; Hoepfner, K; Klimkovich, T; Klingebiel, D; Kreuzer, P; Lanske, D; Magass, C; Merschmeyer, M; Meyer, A; Papacz, P; Pieta, H; Reithler, H; Schmitz, S A; Sonnenschein, L; Steggemann, J; Teyssier, D; Bontenackels, M; Davids, M; Duda, M; Flügge, G; Geenen, H; Giffels, M; Haj Ahmad, W; Heydhausen, D; Kress, T; Kuessel, Y; Linn, A; Nowack, A; Perchalla, L; Pooth, O; Rennefeld, J; Sauerland, P; Stahl, A; Thomas, M; Tornier, D; Zoeller, M H; Aldaya Martin, M; Behrenhoff, W; Behrens, U; Bergholz, M; Bethani, A; Borras, K; Cakir, A; Campbell, A; Castro, E; Dammann, D; Eckerlin, G; Eckstein, D; Flossdorf, A; Flucke, G; Geiser, A; Hauk, J; Jung, H; Kasemann, M; Katkov, I; Katsas, P; Kleinwort, C; Kluge, H; Knutsson, A; Krämer, M; Krücker, D; Kuznetsova, E; Lange, W; Lohmann, W; Mankel, R; Marienfeld, M; Melzer-Pellmann, I-A; Meyer, A B; Mnich, J; Mussgiller, A; Olzem, J; Pitzl, D; Raspereza, A; Raval, A; Rosin, M; Schmidt, R; Schoerner-Sadenius, T; Sen, N; Spiridonov, A; Stein, M; Tomaszewska, J; Walsh, R; Wissing, C; Autermann, C; Blobel, V; Bobrovskyi, S; Draeger, J; Enderle, H; Gebbert, U; Kaschube, K; Kaussen, G; Klanner, R; Lange, J; Mura, B; Naumann-Emme, S; Nowak, F; Pietsch, N; Sander, C; Schettler, H; Schleper, P; Schröder, M; Schum, T; Schwandt, J; Stadie, H; Steinbrück, G; Thomsen, J; Barth, C; Bauer, J; Buege, V; Chwalek, T; De Boer, W; Dierlamm, A; Dirkes, G; Feindt, M; Gruschke, J; Hackstein, C; Hartmann, F; Heinrich, M; Held, H; Hoffmann, K H; Honc, S; Komaragiri, J R; Kuhr, T; Martschei, D; Mueller, S; Müller, Th; Niegel, M; Oberst, O; Oehler, A; Ott, J; Peiffer, T; Quast, G; Rabbertz, K; Ratnikov, F; Ratnikova, N; Renz, M; Saout, C; Scheurer, A; Schieferdecker, P; Schilling, F-P; Schmanau, M; Schott, G; Simonis, H J; Stober, F M; Troendle, D; Wagner-Kuhr, J; Weiler, T; Zeise, M; Zhukov, V; Ziebarth, E B; Daskalakis, G; Geralis, T; Kesisoglou, S; Kyriakis, A; Loukas, D; Manolakos, I; Markou, A; Markou, C; Mavrommatis, C; Ntomari, E; Petrakou, E; Gouskos, L; Mertzimekis, T J; Panagiotou, A; Stiliaris, E; Evangelou, I; Foudas, C; Kokkas, P; Manthos, N; Papadopoulos, I; Patras, V; Triantis, F A; Aranyi, A; Bencze, G; Boldizsar, L; Hajdu, C; Hidas, P; Horvath, D; Kapusi, A; Krajczar, K; Sikler, F; Veres, G I; Vesztergombi, G; Beni, N; Molnar, J; Palinkas, J; Szillasi, Z; Veszpremi, V; Raics, P; Trocsanyi, Z L; Ujvari, B; Bansal, S; Beri, S B; Bhatnagar, V; Dhingra, N; Gupta, R; Jindal, M; Kaur, M; Kohli, J M; Mehta, M Z; Nishu, N; Saini, L K; Sharma, A; Singh, A P; Singh, J B; Singh, S P; Ahuja, S; Bhattacharya, S; Choudhary, B C; Gupta, P; Jain, S; Jain, S; Kumar, A; Ranjan, K; 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Lacroix, F; Malek, M; O'Brien, C; Silvestre, C; Smoron, A; Strom, D; Varelas, N; Akgun, U; Albayrak, E A; Bilki, B; Clarida, W; Duru, F; Lae, C K; McCliment, E; Merlo, J-P; Mermerkaya, H; Mestvirishvili, A; Moeller, A; Nachtman, J; Newsom, C R; Norbeck, E; Olson, J; Onel, Y; Ozok, F; Sen, S; Wetzel, J; Yetkin, T; Yi, K; Barnett, B A; Blumenfeld, B; Bonato, A; Eskew, C; Fehling, D; Giurgiu, G; Gritsan, A V; Guo, Z J; Hu, G; Maksimovic, P; Rappoccio, S; Swartz, M; Tran, N V; Whitbeck, A; Baringer, P; Bean, A; Benelli, G; Grachov, O; Kenny Iii, R P; Murray, M; Noonan, D; Sanders, S; Wood, J S; Zhukova, V; Barfuss, A f; Bolton, T; Chakaberia, I; Ivanov, A; Khalil, S; Makouski, M; Maravin, Y; Shrestha, S; Svintradze, I; Wan, Z; Gronberg, J; Lange, D; Wright, D; Baden, A; Boutemeur, M; Eno, S C; Ferencek, D; Gomez, J A; Hadley, N J; Kellogg, R G; Kirn, M; Lu, Y; Mignerey, A C; Rossato, K; Rumerio, P; Santanastasio, F; Skuja, A; Temple, J; Tonjes, M B; Tonwar, S C; Twedt, E; Alver, B; Bauer, G; Bendavid, J; Busza, W; Butz, E; Cali, I A; Chan, M; Dutta, V; Everaerts, P; Gomez Ceballos, G; Goncharov, M; Hahn, K A; Harris, P; Kim, Y; Klute, M; Lee, Y-J; Li, W; Loizides, C; Luckey, P D; Ma, T; Nahn, S; Paus, C; Ralph, D; Roland, C; Roland, G; Rudolph, M; Stephans, G S F; Stöckli, F; Sumorok, K; Sung, K; Wenger, E A; Xie, S; Yang, M; Yilmaz, Y; Yoon, A S; Zanetti, M; Cooper, S I; Cushman, P; Dahmes, B; De Benedetti, A; Dudero, P R; Franzoni, G; Haupt, J; Klapoetke, K; Kubota, Y; Mans, J; Rekovic, V; Rusack, R; Sasseville, M; Singovsky, A; Cremaldi, L M; Godang, R; Kroeger, R; Perera, L; Rahmat, R; Sanders, D A; Summers, D; Bloom, K; Bose, S; Butt, J; Claes, D R; Dominguez, A; Eads, M; Keller, J; Kelly, T; Kravchenko, I; Lazo-Flores, J; Malbouisson, H; Malik, S; Snow, G R; Baur, U; Godshalk, A; Iashvili, I; Jain, S; Kharchilava, A; Kumar, A; Shipkowski, S P; Smith, K; Alverson, G; Barberis, E; Baumgartel, D; Boeriu, O; Chasco, M; Reucroft, S; Swain, J; Trocino, D; Wood, D; Zhang, J; Anastassov, A; Kubik, A; Odell, N; Ofierzynski, R A; Pollack, B; Pozdnyakov, A; Schmitt, M; Stoynev, S; Velasco, M; Won, S; Antonelli, L; Berry, D; Hildreth, M; Jessop, C; Karmgard, D J; Kolb, J; Kolberg, T; Lannon, K; Luo, W; Lynch, S; Marinelli, N; Morse, D M; Pearson, T; Ruchti, R; Slaunwhite, J; Valls, N; Wayne, M; Ziegler, J; Bylsma, B; Durkin, L S; Gu, J; Hill, C; Killewald, P; Kotov, K; Ling, T Y; Rodenburg, M; Williams, G; Adam, N; Berry, E; Elmer, P; Gerbaudo, D; Halyo, V; Hebda, P; Hunt, A; Jones, J; Laird, E; Lopes Pegna, D; Marlow, D; Medvedeva, T; Mooney, M; Olsen, J; Piroué, P; Quan, X; Saka, H; Stickland, D; Tully, C; Werner, J S; Zuranski, A; Acosta, J G; Huang, X T; Lopez, A; Mendez, H; Oliveros, S; Ramirez Vargas, J E; Zatserklyaniy, A; Alagoz, E; Barnes, V E; Bolla, G; Borrello, L; Bortoletto, D; Everett, A; Garfinkel, A F; Gutay, L; Hu, Z; Jones, M; Koybasi, O; Kress, M; Laasanen, A T; Leonardo, N; Liu, C; Maroussov, V; Merkel, P; Miller, D H; Neumeister, N; Shipsey, I; Silvers, D; Svyatkovskiy, A; Yoo, H D; Zablocki, J; Zheng, Y; Jindal, P; Parashar, N; Boulahouache, C; Cuplov, V; Ecklund, K M; Geurts, F J M; Padley, B P; Redjimi, R; Roberts, J; Zabel, J; Betchart, B; Bodek, A; Chung, Y S; Covarelli, R; de Barbaro, P; Demina, R; Eshaq, Y; Flacher, H; Garcia-Bellido, A; Goldenzweig, P; Gotra, Y; Han, J; Harel, A; Miner, D C; Orbaker, D; Petrillo, G; Vishnevskiy, D; Zielinski, M; Bhatti, A; Ciesielski, R; Demortier, L; Goulianos, K; Lungu, G; Malik, S; Mesropian, C; Yan, M; Atramentov, O; Barker, A; Duggan, D; Gershtein, Y; Gray, R; Halkiadakis, E; Hidas, D; Hits, D; Lath, A; Panwalkar, S; Patel, R; Richards, A; Rose, K; Schnetzer, S; Somalwar, S; Stone, R; Thomas, S; Cerizza, G; Hollingsworth, M; Spanier, S; Yang, Z C; York, A; Eusebi, R; Gilmore, J; Gurrola, A; Kamon, T; Khotilovich, V; Montalvo, R; Osipenkov, I; Pakhotin, Y; Pivarski, J; Safonov, A; Sengupta, S; Tatarinov, A; Toback, D; Weinberger, M; Akchurin, N; Bardak, C; Damgov, J; Jeong, C; Kovitanggoon, K; Lee, S W; Mane, P; Roh, Y; Sill, A; Volobouev, I; Wigmans, R; Yazgan, E; Appelt, E; Brownson, E; Engh, D; Florez, C; Gabella, W; Issah, M; Johns, W; Kurt, P; Maguire, C; Melo, A; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Arenton, M W; Balazs, M; Boutle, S; Cox, B; Francis, B; Hirosky, R; Ledovskoy, A; Lin, C; Neu, C; Yohay, R; Gollapinni, S; Harr, R; Karchin, P E; Lamichhane, P; Mattson, M; Milstène, C; Sakharov, A; Anderson, M; Bachtis, M; Bellinger, J N; Carlsmith, D; Dasu, S; Efron, J; Flood, K; Gray, L; Grogg, K S; Grothe, M; Hall-Wilton, R; Herndon, M; Klabbers, P; Klukas, J; Lanaro, A; Lazaridis, C; Leonard, J; Loveless, R; Mohapatra, A; Palmonari, F; Reeder, D; Ross, I; Savin, A; Smith, W H; Swanson, J; Weinberg, M

2011-07-08

A first measurement of the polarization of W bosons with large transverse momenta in pp collisions is presented. The measurement is based on 36 pb⁻¹ of data recorded at √s = 7 TeV by the CMS detector at the LHC. The left-handed, right-handed, and longitudinal polarization fractions (f(L), f(R), and f₀, respectively) of W bosons with transverse momenta larger than 50 GeV are determined by using decays to both electrons and muons. The muon final state yields the most precise measurement: (f(L) - f(R))⁻ = 0.240 ± 0.036(stat) ± 0.031(syst) and f₀⁻ = 0.183 ± 0.087(stat) ± 0.123(syst) for negatively charged W bosons and (f(L) - f(R))⁺ = 0.310 ± 0.036(stat) ± 0.017(syst) and f₀⁺ = 0.171 ± 0.085(stat) ± 0.099(syst) for positively charged W bosons. This establishes, for the first time, that W bosons produced in pp collisions with large transverse momenta are predominantly left-handed, as expected in the standard model.

STrategically Acquired Gradient Echo (STAGE) imaging, part I: Creating enhanced T1 contrast and standardized susceptibility weighted imaging and quantitative susceptibility mapping.

Science.gov (United States)

Chen, Yongsheng; Liu, Saifeng; Wang, Yu; Kang, Yan; Haacke, E Mark

2018-02-01

To provide whole brain grey matter (GM) to white matter (WM) contrast enhanced T1W (T1WE) images, multi-echo quantitative susceptibility mapping (QSM), proton density (PD) weighted images, T1 maps, PD maps, susceptibility weighted imaging (SWI), and R2* maps with minimal misregistration in scanning times creating enhanced GM/WM contrast (the T1WE). The proposed T1WE image was created from a combination of the proton density weighted (6°, PDW) and T1W (24°) images and corrected for RF transmit field variations. Prior to the QSM calculation, a multi-echo phase unwrapping strategy was implemented using the unwrapped short echo to unwrap the longer echo to speed up computation. R2* maps were used to mask deep grey matter and veins during the iterative QSM calculation. A weighted-average sum of susceptibility maps was generated to increase the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR). The proposed T1WE image has a significantly improved CNR both for WM to deep GM and WM to cortical GM compared to the acquired T1W image (the first echo of 24° scan) and the T1MPRAGE image. The weighted-average susceptibility maps have 80±26%, 55±22%, 108±33% SNR increases across the ten subjects compared to the single echo result of 17.5ms for the putamen, caudate nucleus, and globus pallidus, respectively. STAGE imaging offers the potential to create a standardized brain imaging protocol providing four pieces of quantitative tissue property information and multiple types of qualitative information in just 5min. Published by Elsevier Inc.

Impact of motion compensation and partial volume correction for 18F-NaF PET/CT imaging of coronary plaque

Science.gov (United States)

Cal-González, J.; Tsoumpas, C.; Lassen, M. L.; Rasul, S.; Koller, L.; Hacker, M.; Schäfers, K.; Beyer, T.

2018-01-01

Recent studies have suggested that 18F-NaF-PET enables visualization and quantification of plaque micro-calcification in the coronary tree. However, PET imaging of plaque calcification in the coronary arteries is challenging because of the respiratory and cardiac motion as well as partial volume effects. The objective of this work is to implement an image reconstruction framework, which incorporates compensation for respiratory as well as cardiac motion (MoCo) and partial volume correction (PVC), for cardiac 18F-NaF PET imaging in PET/CT. We evaluated the effect of MoCo and PVC on the quantification of vulnerable plaques in the coronary arteries. Realistic simulations (Biograph TPTV, Biograph mCT) and phantom acquisitions (Biograph mCT) were used for these evaluations. Different uptake values in the calcified plaques were evaluated in the simulations, while three ‘plaque-type’ lesions of 36, 31 and 18 mm3 were included in the phantom experiments. After validation, the MoCo and PVC methods were applied in four pilot NaF-PET patient studies. In all cases, the MoCo-based image reconstruction was performed using the STIR software. The PVC was obtained from a local projection (LP) method, previously evaluated in preclinical and clinical PET. The results obtained show a significant increase of the measured lesion-to-background ratios (LBR) in the MoCo  +  PVC images. These ratios were further enhanced when using directly the tissue-activities from the LP method, making this approach more suitable for the quantitative evaluation of coronary plaques. When using the LP method on the MoCo images, LBR increased between 200% and 1119% in the simulated data, between 212% and 614% in the phantom experiments and between 46% and 373% in the plaques with positive uptake observed in the pilot patients. In conclusion, we have built and validated a STIR framework incorporating MoCo and PVC for 18F-NaF PET imaging of coronary plaques. First results indicate an improved

DEEP U BAND AND R IMAGING OF GOODS-SOUTH: OBSERVATIONS, DATA REDUCTION AND FIRST RESULTS ,

International Nuclear Information System (INIS)

Nonino, M.; Cristiani, S.; Vanzella, E.; Dickinson, M.; Reddy, N.; Rosati, P.; Grazian, A.; Giavalisco, M.; Kuntschner, H.; Fosbury, R. A. E.; Daddi, E.; Cesarsky, C.

2009-01-01

We present deep imaging in the U band covering an area of 630 arcmin 2 centered on the southern field of the Great Observatories Origins Deep Survey (GOODS). The data were obtained with the VIMOS instrument at the European Southern Observatory (ESO) Very Large Telescope. The final images reach a magnitude limit U lim ∼ 29.8 (AB, 1σ, in a 1'' radius aperture), and have good image quality, with full width at half-maximum ∼0.''8. They are significantly deeper than previous U-band images available for the GOODS fields, and better match the sensitivity of other multiwavelength GOODS photometry. The deeper U-band data yield significantly improved photometric redshifts, especially in key redshift ranges such as 2 lim ∼ 29 (AB, 1σ, 1'' radius aperture), and image quality ∼0.''75. We discuss the strategies for the observations and data reduction, and present the first results from the analysis of the co-added images.

Image annotation by deep neural networks with attention shaping

Science.gov (United States)

Zheng, Kexin; Lv, Shaohe; Ma, Fang; Chen, Fei; Jin, Chi; Dou, Yong

2017-07-01

Image annotation is a task of assigning semantic labels to an image. Recently, deep neural networks with visual attention have been utilized successfully in many computer vision tasks. In this paper, we show that conventional attention mechanism is easily misled by the salient class, i.e., the attended region always contains part of the image area describing the content of salient class at different attention iterations. To this end, we propose a novel attention shaping mechanism, which aims to maximize the non-overlapping area between consecutive attention processes by taking into account the history of previous attention vectors. Several weighting polices are studied to utilize the history information in different manners. In two benchmark datasets, i.e., PASCAL VOC2012 and MIRFlickr-25k, the average precision is improved by up to 10% in comparison with the state-of-the-art annotation methods.

Deep gray matter demyelination detected by magnetization transfer ratio in the cuprizone model.

Directory of Open Access Journals (Sweden)

Sveinung Fjær

Full Text Available In multiple sclerosis (MS, the correlation between lesion load on conventional magnetic resonance imaging (MRI and clinical disability is weak. This clinico-radiological paradox might partly be due to the low sensitivity of conventional MRI to detect gray matter demyelination. Magnetization transfer ratio (MTR has previously been shown to detect white matter demyelination in mice. In this study, we investigated whether MTR can detect gray matter demyelination in cuprizone exposed mice. A total of 54 female C57BL/6 mice were split into one control group ( and eight cuprizone exposed groups ([Formula: see text]. The mice were exposed to [Formula: see text] (w/w cuprizone for up to six weeks. MTR images were obtained at a 7 Tesla Bruker MR-scanner before cuprizone exposure, weekly for six weeks during cuprizone exposure, and once two weeks after termination of cuprizone exposure. Immunohistochemistry staining for myelin (anti-Proteolopid Protein and oligodendrocytes (anti-Neurite Outgrowth Inhibitor Protein A was obtained after each weekly scanning. Rates of MTR change and correlations between MTR values and histological findings were calculated in five brain regions. In the corpus callosum and the deep gray matter a significant rate of MTR value decrease was found, [Formula: see text] per week ([Formula: see text] and [Formula: see text] per week ([Formula: see text] respectively. The MTR values correlated to myelin loss as evaluated by immunohistochemistry (Corpus callosum: [Formula: see text]. Deep gray matter: [Formula: see text], but did not correlate to oligodendrocyte density. Significant results were not found in the cerebellum, the olfactory bulb or the cerebral cortex. This study shows that MTR can be used to detect demyelination in the deep gray matter, which is of particular interest for imaging of patients with MS, as deep gray matter demyelination is common in MS, and is not easily detected on conventional clinical MRI.

ToF-SIMS measurements with topographic information in combined images.

Science.gov (United States)

Koch, Sabrina; Ziegler, Georg; Hutter, Herbert

2013-09-01

In 2D and 3D time-of-flight secondary ion mass spectrometric (ToF-SIMS) analysis, accentuated structures on the sample surface induce distorted element distributions in the measurement. The origin of this effect is the 45° incidence angle of the analysis beam, recording planar images with distortion of the sample surface. For the generation of correct element distributions, these artifacts associated with the sample surface need to be eliminated by measuring the sample surface topography and applying suitable algorithms. For this purpose, the next generation of ToF-SIMS instruments will feature a scanning probe microscope directly implemented in the sample chamber which allows the performance of topography measurements in situ. This work presents the combination of 2D and 3D ToF-SIMS analysis with topographic measurements by ex situ techniques such as atomic force microscopy (AFM), confocal microscopy (CM), and digital holographic microscopy (DHM). The concept of the combination of topographic and ToF-SIMS measurements in a single representation was applied to organic and inorganic samples featuring surface structures in the nanometer and micrometer ranges. The correct representation of planar and distorted ToF-SIMS images was achieved by the combination of topographic data with images of 2D as well as 3D ToF-SIMS measurements, using either AFM, CM, or DHM for the recording of topographic data.

Motor and Nonmotor Circuitry Activation Induced by Subthalamic Nucleus Deep Brain Stimulation in Patients With Parkinson Disease: Intraoperative Functional Magnetic Resonance Imaging for Deep Brain Stimulation.

Science.gov (United States)

Knight, Emily J; Testini, Paola; Min, Hoon-Ki; Gibson, William S; Gorny, Krzysztof R; Favazza, Christopher P; Felmlee, Joel P; Kim, Inyong; Welker, Kirk M; Clayton, Daniel A; Klassen, Bryan T; Chang, Su-youne; Lee, Kendall H

2015-06-01

To test the hypothesis suggested by previous studies that subthalamic nucleus (STN) deep brain stimulation (DBS) in patients with Parkinson disease would affect the activity of motor and nonmotor networks, we applied intraoperative functional magnetic resonance imaging (fMRI) to patients receiving DBS. Ten patients receiving STN DBS for Parkinson disease underwent intraoperative 1.5-T fMRI during high-frequency stimulation delivered via an external pulse generator. The study was conducted between January 1, 2013, and September 30, 2014. We observed blood oxygen level-dependent (BOLD) signal changes (false discovery rate <0.001) in the motor circuitry (including the primary motor, premotor, and supplementary motor cortices; thalamus; pedunculopontine nucleus; and cerebellum) and in the limbic circuitry (including the cingulate and insular cortices). Activation of the motor network was observed also after applying a Bonferroni correction (P<.001) to the data set, suggesting that across patients, BOLD changes in the motor circuitry are more consistent compared with those occurring in the nonmotor network. These findings support the modulatory role of STN DBS on the activity of motor and nonmotor networks and suggest complex mechanisms as the basis of the efficacy of this treatment modality. Furthermore, these results suggest that across patients, BOLD changes in the motor circuitry are more consistent than those in the nonmotor network. With further studies combining the use of real-time intraoperative fMRI with clinical outcomes in patients treated with DBS, functional imaging techniques have the potential not only to elucidate the mechanisms of DBS functioning but also to guide and assist in the surgical treatment of patients affected by movement and neuropsychiatric disorders. clinicaltrials.gov Identifier: NCT01809613. Copyright © 2015 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

Deep learning for the detection of barchan dunes in satellite images

Science.gov (United States)

Azzaoui, A. M.; Adnani, M.; Elbelrhiti, H.; Chaouki, B. E. K.; Masmoudi, L.

2017-12-01

Barchan dunes are known to be the fastest moving sand dunes in deserts as they form under unidirectional winds and limited sand supply over a firm coherent basement (Elbelrhiti and Hargitai,2015). They were studied in the context of natural hazard monitoring as they could be a threat to human activities and infrastructures. Also, they were studied as a natural phenomenon occurring in other planetary landforms such as Mars or Venus (Bourke et al., 2010). Our region of interest was located in a desert region in the south of Morocco, in a barchan dunes corridor next to the town of Tarfaya. This region which is part of the Sahara desert contained thousands of barchans; which limits the number of dunes that could be studied during field missions. Therefore, we chose to monitor barchan dunes with satellite imagery, which can be seen as a complementary approach to field missions. We collected data from the Sentinel platform (https://scihub.copernicus.eu/dhus/); we used a machine learning method as a basis for the detection of barchan dunes positions in the satellite image. We trained a deep learning model on a mid-sized dataset that contained blocks representing images of barchan dunes, and images of other desert features, that we collected by cropping and annotating the source image. During testing, we browsed the satellite image with a gliding window that evaluated each block, and then produced a probability map. Finally, a threshold on the latter map exposed the location of barchan dunes. We used a subsample of data to train the model and we gradually incremented the size of the training set to get finer results and avoid over fitting. The positions of barchan dunes were successfully detected and deep learning was an effective method for this application. Sentinel-2 images were chosen for their availability and good temporal resolution, which will allow the tracking of barchan dunes in future work. While Sentinel images had sufficient spatial resolution for the

Deep Ly alpha imaging of two z=2.04 GRB host galaxy fields

DEFF Research Database (Denmark)

Fynbo, J.P.U.; Møller, Per; Thomsen, Bente

2002-01-01

We report on the results of deep narrow-band Lyalpha and broad-band U and I imaging of the fields of two Gamma-Ray bursts at redshift z = 2.04 (GRB 000301C and GRB 000926). We find that the host galaxy of GRB 000926 is an extended (more than 2 arcsec), strong Lyalpha emitter with a rest-frame equ......We report on the results of deep narrow-band Lyalpha and broad-band U and I imaging of the fields of two Gamma-Ray bursts at redshift z = 2.04 (GRB 000301C and GRB 000926). We find that the host galaxy of GRB 000926 is an extended (more than 2 arcsec), strong Lyalpha emitter with a rest...... - I colour than the eastern component, suggesting the presence of at least some dust. We do not detect the host galaxy of GRB 000301C in neither Lyalpha emission nor in U and I broad-band images. The strongest limit comes from combining the narrow and U-band imaging where we infer a limit of U...

F-19 MR imaging in vivo with FTPA

International Nuclear Information System (INIS)

Iriguchi, N.; Miyazaki, T.; Hasegawa, J.; Yamamoto, T.; Yamai, S.; Veshima, Y.; Iwasaki, A.; Toyoshima, H.; Maki, T.

1986-01-01

Perfluorotripropylamine (FTPA), together with perfluorodecalin (FDC), makes a blood substitute which has been known to be clinically safe for certain applications. A 1-m-bore, 2-T system (Asahi Mark-J Super 200) was used to image a young rabbit. After intravenous administration of FTPA emulsion, F-19 imaging was carried out. One hour after FTPA administration, a faint but certain image was obtained. Twenty-four hours after the administration of FTPA. the liver, spleen, and bone marrow were clearly recognized. Each image was obtained in 2 minutes

Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds

Science.gov (United States)

Maltezos, Evangelos; Doulamis, Nikolaos; Doulamis, Anastasios; Ioannidis, Charalabos

2017-10-01

Automatic extraction of buildings from remote sensing data is an attractive research topic, useful for several applications, such as cadastre and urban planning. This is mainly due to the inherent artifacts of the used data and the differences in viewpoint, surrounding environment, and complex shape and size of the buildings. This paper introduces an efficient deep learning framework based on convolutional neural networks (CNNs) toward building extraction from orthoimages. In contrast to conventional deep approaches in which the raw image data are fed as input to the deep neural network, in this paper the height information is exploited as an additional feature being derived from the application of a dense image matching algorithm. As test sites, several complex urban regions of various types of buildings, pixel resolutions and types of data are used, located in Vaihingen in Germany and in Perissa in Greece. Our method is evaluated using the rates of completeness, correctness, and quality and compared with conventional and other "shallow" learning paradigms such as support vector machines. Experimental results indicate that a combination of raw image data with height information, feeding as input to a deep CNN model, provides potentials in building detection in terms of robustness, flexibility, and efficiency.

Molecular imaging needles: dual-modality optical coherence tomography and fluorescence imaging of labeled antibodies deep in tissue

Science.gov (United States)

Scolaro, Loretta; Lorenser, Dirk; Madore, Wendy-Julie; Kirk, Rodney W.; Kramer, Anne S.; Yeoh, George C.; Godbout, Nicolas; Sampson, David D.; Boudoux, Caroline; McLaughlin, Robert A.

2015-01-01

Molecular imaging using optical techniques provides insight into disease at the cellular level. In this paper, we report on a novel dual-modality probe capable of performing molecular imaging by combining simultaneous three-dimensional optical coherence tomography (OCT) and two-dimensional fluorescence imaging in a hypodermic needle. The probe, referred to as a molecular imaging (MI) needle, may be inserted tens of millimeters into tissue. The MI needle utilizes double-clad fiber to carry both imaging modalities, and is interfaced to a 1310-nm OCT system and a fluorescence imaging subsystem using an asymmetrical double-clad fiber coupler customized to achieve high fluorescence collection efficiency. We present, to the best of our knowledge, the first dual-modality OCT and fluorescence needle probe with sufficient sensitivity to image fluorescently labeled antibodies. Such probes enable high-resolution molecular imaging deep within tissue. PMID:26137379

Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures.

Science.gov (United States)

Trullo, Roger; Petitjean, Caroline; Nie, Dong; Shen, Dinggang; Ruan, Su

2017-09-01

Computed Tomography (CT) is the standard imaging technique for radiotherapy planning. The delineation of Organs at Risk (OAR) in thoracic CT images is a necessary step before radiotherapy, for preventing irradiation of healthy organs. However, due to low contrast, multi-organ segmentation is a challenge. In this paper, we focus on developing a novel framework for automatic delineation of OARs. Different from previous works in OAR segmentation where each organ is segmented separately, we propose two collaborative deep architectures to jointly segment all organs, including esophagus, heart, aorta and trachea. Since most of the organ borders are ill-defined, we believe spatial relationships must be taken into account to overcome the lack of contrast. The aim of combining two networks is to learn anatomical constraints with the first network, which will be used in the second network, when each OAR is segmented in turn. Specifically, we use the first deep architecture, a deep SharpMask architecture, for providing an effective combination of low-level representations with deep high-level features, and then take into account the spatial relationships between organs by the use of Conditional Random Fields (CRF). Next, the second deep architecture is employed to refine the segmentation of each organ by using the maps obtained on the first deep architecture to learn anatomical constraints for guiding and refining the segmentations. Experimental results show superior performance on 30 CT scans, comparing with other state-of-the-art methods.

Deep inspiration breath-hold radiotherapy for lung cancer: impact on image quality and registration uncertainty in cone beam CT image guidance

DEFF Research Database (Denmark)

Josipovic, Mirjana; Persson, Gitte F; Bangsgaard, Jens Peter

2016-01-01

OBJECTIVE: We investigated the impact of deep inspiration breath-hold (DIBH) and tumour baseline shifts on image quality and registration uncertainty in image-guided DIBH radiotherapy (RT) for locally advanced lung cancer. METHODS: Patients treated with daily cone beam CT (CBCT)-guided free...

Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection.

Science.gov (United States)

Hu, Weiming; Fan, Yabo; Xing, Junliang; Sun, Liang; Cai, Zhaoquan; Maybank, Stephen

2018-09-01

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.

18F-F.D.G. PET imaging of infection and inflammation: intestinal, prosthesis replacements, fibrosis, sarcoidosis, tuberculosis.

International Nuclear Information System (INIS)

Fernandez, A.; Cortes, M.; Caresia, A.P.; Juan, R. de; Vidaller, A.; Mana, J.; Martinez-Yelamos, S.; Gamez, C.

2008-01-01

Nuclear medicine plays an important role in the evaluation of infection and inflammation. A variety of diagnostic methods are available for imaging this inflammation and infection, most notably computed tomography, 68 Ga scintigraphy or radionuclide labeled leucocytes. Fluorine 18 fluorodeoxyglucose ( 18 F-F.D.G.) is a readily available radiotracer that offers rapid, exquisitely sensitive high-resolution images by positron emission tomography (PET). Inflammation can be acute or chronic, the former showing predominantly neutrophilic granulocyte infiltrates, whereas in the latter, macrophages predominate. F.D.G. uptake in infection is based on the fact that mononuclear cells and granulocytes use large quantities of glucose by way of the hexose monophosphate shunts. 18 F-F.D.G. PET accurately helps diagnose spinal osteomyelitis, diabetic foot and in inflammatory conditions such as sarcoidosis and tuberculosis.(it appears to be useful for defining the extent of disease and monitoring response to treatment). 18 F-F.D.G. PET can also help localize the source of fever of undetermined origin, thereby guiding additional testing. 18 F-F.D.G. PET may be of limited usefulness in postoperative patients and in patients with a failed joint prosthesis or bowel inflammatory disease. In this review, we will focus on the role of 18 F-F.D.G. PET in the management of patients with inflammation or suspected or confirmed infection

Quicksilver: Fast predictive image registration - A deep learning approach.

Science.gov (United States)

Yang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, Marc

2017-09-01

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. Copyright © 2017 Elsevier Inc. All rights reserved.

Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks

Directory of Open Access Journals (Sweden)

Farhan Hussain

2016-01-01

Full Text Available Nowadays many camera-based advanced driver assistance systems (ADAS have been introduced to assist the drivers and ensure their safety under various driving conditions. One of the problems faced by drivers is the faded scene visibility and lower contrast while driving in foggy conditions. In this paper, we present a novel approach to provide a solution to this problem by employing deep neural networks. We assume that the fog in an image can be mathematically modeled by an unknown complex function and we utilize the deep neural network to approximate the corresponding mathematical model for the fog. The advantages of our technique are as follows: (i its real-time operation and (ii being based on minimal input, that is, a single image, and exhibiting robustness/generalization for various unseen image data. Experiments carried out on various synthetic images indicate that our proposed technique has the abilities to approximate the corresponding fog function reasonably and remove it for better visibility and safety.

Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning

Directory of Open Access Journals (Sweden)

Liu Ying

2017-01-01

Full Text Available Ship detection and classification is critical for national maritime security and national defense. Although some SAR (Synthetic Aperture Radar image-based ship detection approaches have been proposed and used, they are not able to satisfy the requirement of real-world applications as the number of SAR sensors is limited, the resolution is low, and the revisit cycle is long. As massive optical remote sensing images of high resolution are available, ship detection and classification on theses images is becoming a promising technique, and has attracted great attention on applications including maritime security and traffic control. Some digital image processing methods have been proposed to detect ships in optical remote sensing images, but most of them face difficulty in terms of accuracy, performance and complexity. Recently, an autoencoder-based deep neural network with extreme learning machine was proposed, but it cannot meet the requirement of real-world applications as it only works with simple and small-scaled data sets. Therefore, in this paper, we propose a novel ship detection and classification approach which utilizes deep convolutional neural network (CNN as the ship classifier. The performance of our proposed ship detection and classification approach was evaluated on a set of images downloaded from Google Earth at the resolution 0.5m. 99% detection accuracy and 95% classification accuracy were achieved. In model training, 75× speedup is achieved on 1 Nvidia Titanx GPU.

In vivo deep brain imaging of rats using oral-cavity illuminated photoacoustic computed tomography

Science.gov (United States)

Lin, Li; Xia, Jun; Wong, Terence T. W.; Zhang, Ruiying; Wang, Lihong V.

2015-03-01

We demonstrate, by means of internal light delivery, photoacoustic imaging of the deep brain of rats in vivo. With fiber illumination via the oral cavity, we delivered light directly into the bottom of the brain, much more than can be delivered by external illumination. The study was performed using a photoacoustic computed tomography (PACT) system equipped with a 512-element full-ring transducer array, providing a full two-dimensional view aperture. Using internal illumination, the PACT system provided clear cross sectional photoacoustic images from the palate to the middle brain of live rats, revealing deep brain structures such as the hypothalamus, brain stem, and cerebral medulla.

{sup 18}F-FDOPA PET/CT imaging of insulinoma revisited

Energy Technology Data Exchange (ETDEWEB)

Imperiale, Alessio; Namer, Izzie-Jacques [University Hospitals of Strasbourg, Department of Biophysics and Nuclear Medicine, Strasbourg (France); University of Strasbourg/CNRS and FMTS, Faculty of Medicine, ICube - UMR 7357, Strasbourg (France); Sebag, Frederic [Aix-Marseille University, Department of Endocrine Surgery, La Timone University Hospital, Marseille (France); Vix, Michel [University of Strasbourg, Department of General, Digestive, and Endocrine Surgery, IRCAD-IHU, Strasbourg (France); Castinetti, Frederic [Aix-Marseille University, Department of Endocrinology, Diabetes and Metabolic Disorders, La Timone University Hospital, Marseille (France); Kessler, Laurence; Moreau, Francois [University of Strasbourg, Department of Diabetology, University Hospital of Strasbourg, Strasbourg (France); Bachellier, Philippe [University Hospitals of Strasbourg, Department of Visceral Surgery and Transplantation, Strasbourg (France); Guillet, Benjamin; Mundler, Olivier [Aix-Marseille University, Department of Nuclear Medicine, La Timone University Hospital, CERIMED, Marseille (France); Taieb, David [Aix-Marseille University, Department of Nuclear Medicine, La Timone University Hospital, CERIMED, Marseille (France); Aix-Marseille University, Biophysics and Nuclear Medecine, La Timone University Hospital, European Center for Research in Medical Imaging, Marseille (France)

2014-11-01

{sup 18}F-FDOPA PET imaging is increasingly used in the work-up of patients with neuroendocrine tumours. It has been shown to be of limited value in localizing pancreatic insulin-secreting tumours in adults with hyperinsulinaemic hypoglycaemia (HH) mainly due to {sup 18}F-FDOPA uptake by the whole pancreatic gland. The objective of this study was to review our experience with {sup 18}F-FDOPA PET/CT imaging with carbidopa (CD) premedication in patients with HH in comparison with PET/CT studies performed without CD premedication in an independent population. A retrospective study including 16 HH patients who were investigated between January 2011 and December 2013 using {sup 18}F-FDOPA PET/CT (17 examinations) in two academic endocrine tumour centres was conducted. All PET/CT examinations were performed under CD premedication (200 mg orally, 1 - 2 h prior to tracer injection). The PET/CT acquisition protocol included an early acquisition (5 min after {sup 18}F-FDOPA injection) centred over the upper abdomen and a delayed whole-body acquisition starting 20 - 30 min later. An independent series of eight consecutive patients with HH and investigated before 2011 were considered for comparison. All patients had a reference whole-body PET/CT scan performed about 1 h after {sup 18}F-FDOPA injection. In all cases, PET/CT was performed without CD premedication. In the study group, {sup 18}F-FDOPA PET/CT with CD premedication was positive in 8 out of 11 patients with histologically proven insulinoma (73 %). All {sup 18}F-FDOPA PET/CT-avid insulinomas were detected on early images and 5 of 11 (45 %) on delayed ones. The tumour/normal pancreas uptake ratio was not significantly different between early and delayed acquisitions. Considering all patients with HH, including those without imaging evidence of disease, the detection rate of the primary lesions using CD-assisted {sup 18}F-FDOPA PET/CT was 53 %, showing 9 insulinomas in 17 studies performed. In the control group (without

A MULTIWAVELENGTH STUDY OF TADPOLE GALAXIES IN THE HUBBLE ULTRA DEEP FIELD

International Nuclear Information System (INIS)

Straughn, Amber N.; Eufrasio, Rafael T.; Gardner, Jonathan P.; Voyer, Elysse N.; Mello, Duilia de; Soto, Emmaris; Petty, Sara; Kassin, Susan; Ravindranath, Swara

2015-01-01

Multiwavelength data are essential in order to provide a complete picture of galaxy evolution and to inform studies of galaxies’ morphological properties across cosmic time. Here we present the results of a multiwavelength investigation of the morphologies of “tadpole” galaxies at intermediate redshift (0.314 < z < 3.175) in the Hubble Ultra Deep Field. These galaxies were previously selected from deep Hubble Space Telescope (HST) F775W data based on their distinct asymmetric knot-plus-tail morphologies. Here we use deep Wide Field Camera 3 near-infrared imaging in addition to the HST optical data in order to study the rest-frame UV/optical morphologies of these galaxies across the redshift range 0.3 < z < 3.2. This study reveals that the majority of these galaxies do retain their general asymmetric morphology in the rest-frame optical over this redshift range, if not the distinct “tadpole” shape. The average stellar mass of tadpole galaxies is lower than that of field galaxies, with the effect being slightly greater at higher redshift within the errors. Estimated from spectral energy distribution fits, the average age of tadpole galaxies is younger than that of field galaxies in the lower-redshift bin, and the average metallicity is lower (whereas the specific star formation rate for tadpoles is roughly the same as field galaxies across the redshift range probed here). These average effects combined support the conclusion that this subset of galaxies is in an active phase of assembly, either late-stage merging or cold gas accretion causing localized clumpy star formation

A MULTIWAVELENGTH STUDY OF TADPOLE GALAXIES IN THE HUBBLE ULTRA DEEP FIELD

Energy Technology Data Exchange (ETDEWEB)

Straughn, Amber N.; Eufrasio, Rafael T.; Gardner, Jonathan P. [Astrophysics Science Division, Goddard Space Flight Center, Code 665, Greenbelt, MD 20771 (United States); Voyer, Elysse N. [Randstad at Google, 1129 San Antonio Road, Palo Alto, CA (United States); Mello, Duilia de; Soto, Emmaris [Department of Physics, The Catholic University of America, Washington, DC 20064 (United States); Petty, Sara [Department of Physics, Virginia Tech, Blacksburg, VA 24061 (United States); Kassin, Susan; Ravindranath, Swara [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States)

2015-12-01

Multiwavelength data are essential in order to provide a complete picture of galaxy evolution and to inform studies of galaxies’ morphological properties across cosmic time. Here we present the results of a multiwavelength investigation of the morphologies of “tadpole” galaxies at intermediate redshift (0.314 < z < 3.175) in the Hubble Ultra Deep Field. These galaxies were previously selected from deep Hubble Space Telescope (HST) F775W data based on their distinct asymmetric knot-plus-tail morphologies. Here we use deep Wide Field Camera 3 near-infrared imaging in addition to the HST optical data in order to study the rest-frame UV/optical morphologies of these galaxies across the redshift range 0.3 < z < 3.2. This study reveals that the majority of these galaxies do retain their general asymmetric morphology in the rest-frame optical over this redshift range, if not the distinct “tadpole” shape. The average stellar mass of tadpole galaxies is lower than that of field galaxies, with the effect being slightly greater at higher redshift within the errors. Estimated from spectral energy distribution fits, the average age of tadpole galaxies is younger than that of field galaxies in the lower-redshift bin, and the average metallicity is lower (whereas the specific star formation rate for tadpoles is roughly the same as field galaxies across the redshift range probed here). These average effects combined support the conclusion that this subset of galaxies is in an active phase of assembly, either late-stage merging or cold gas accretion causing localized clumpy star formation.

MO-AB-BRA-05: [18F]NaF PET/CT Imaging Biomarkers in Metastatic Prostate Cancer

Energy Technology Data Exchange (ETDEWEB)

Harmon, S; Perk, T; Lin, C; Eickhoff, J; Perlman, S; Liu, G; Jeraj, R [University of Wisconsin Madison, Madison, WI (United States); Choyke, P; Dahut, W; Apolo, A [National Cancer Institute at the National Institutes of Health, Bethesda, MD (United States); Humm, J; Larson, S; Morris, MJ [Memorial Sloan-Kettering Cancer Center, New York, NY (United States)

2016-06-15

Purpose: Clinical use of {sup 18}F-Sodium Fluoride (NaF) PET/CT in metastatic settings often lacks technology to quantitatively measure full disease dynamics due to high tumor burden. This study assesses radiomics-based extraction of NaF PET/CT measures, including global metrics of overall burden and local metrics of disease heterogeneity, in metastatic prostate cancer for correlation to clinical outcomes. Methods: Fifty-six metastatic Castrate-Resistant Prostate Cancer (mCRPC) patients had NaF PET/CT scans performed at baseline and three cycles into chemotherapy (N=16) or androgen-receptor (AR) inhibitors (N=39). A novel technology, Quantitative Total Bone Imaging (QTBI), was used for analysis. Employing hybrid PET/CT segmentation and articulated skeletal-registration, QTBI allows for response assessment of individual lesions. Various SUV metrics were extracted from each lesion (iSUV). Global metrics were extracted from composite lesion-level statistics for each patient (pSUV). Proportion of detected lesions and those with significant response (%-increase or %-decrease) was calculated for each patient based on test-retest limits for iSUV metrics. Cox proportional hazard regression analyses were conducted between imaging metrics and progression-free survival (PFS). Results: Functional burden (pSUV{sub total}) assessed mid-treatment was the strongest univariate predictor of PFS (HR=2.03; p<0.0001). Various global metrics outperformed baseline clinical markers, including fraction of skeletal burden, mean uptake (pSUV{sub mean}), and heterogeneity of average lesion uptake (pSUV{sub hetero}). Of 43 patients with paired baseline/mid-treatment imaging, 40 showed heterogeneity in lesion-level response, containing populations of lesions with both increasing/decreasing metrics. Proportion of lesions with significantly increasing iSUV{sub mean} was highly predictive of clinical PFS (HR=2.0; p=0.0002). Patients exhibiting higher proportion of lesions with decreasing i

Disappearance of the Supergiant Progenitor of SN 2011dh in M51

Science.gov (United States)

Van Dyk, Schuyler D.; Filippenko, Alexei V.; Fox, Ori; Kelly, Patrick; Smith, Nathan

2013-03-01

We report that in Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) observations at F555W and F814W with the UVIS channel, conducted on 2013 March 2 UT as part of our Cycle 20 Snapshot program GO-13029 (PI: A. Filippenko), we have discovered that the yellow supergiant star, identified by Van Dyk et al. (2011, ApJ, 741, L28) and Maund et al. (2011, MNRAS, 739, L37) at the position of the Type IIb SN 2011dh in M51, has vanished.

Color Magnitude Diagrams of Old, Massive GCs in M31

Science.gov (United States)

Caldwell, Nelson; Williams, B.; Dolphin, A. E.; Johnson, L. C.; Weisz, D. R.

2013-01-01

Multicolor stellar photometry of HST data of M31 collected as part of the PHAT project has been performed using the DOLPHOT suite of programs. We present results of color-magnitude diagrams created in F475W and F814W (BI) of more than 50 massive, old clusters. These are clusters in or projected on the disk. We compare the metallicities derived from the color of the giant branch stars with that derived from integrated light spectroscopy. As well, we compare the ages of massive, young clusters with those found from spectra.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

Science.gov (United States)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

In Vivo Deep Tissue Fluorescence and Magnetic Imaging Employing Hybrid Nanostructures.

Science.gov (United States)

Ortgies, Dirk H; de la Cueva, Leonor; Del Rosal, Blanca; Sanz-Rodríguez, Francisco; Fernández, Nuria; Iglesias-de la Cruz, M Carmen; Salas, Gorka; Cabrera, David; Teran, Francisco J; Jaque, Daniel; Martín Rodríguez, Emma

2016-01-20

Breakthroughs in nanotechnology have made it possible to integrate different nanoparticles in one single hybrid nanostructure (HNS), constituting multifunctional nanosized sensors, carriers, and probes with great potential in the life sciences. In addition, such nanostructures could also offer therapeutic capabilities to achieve a wider variety of multifunctionalities. In this work, the encapsulation of both magnetic and infrared emitting nanoparticles into a polymeric matrix leads to a magnetic-fluorescent HNS with multimodal magnetic-fluorescent imaging abilities. The magnetic-fluorescent HNS are capable of simultaneous magnetic resonance imaging and deep tissue infrared fluorescence imaging, overcoming the tissue penetration limits of classical visible-light based optical imaging as reported here in living mice. Additionally, their applicability for magnetic heating in potential hyperthermia treatments is assessed.

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

Science.gov (United States)

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

Science.gov (United States)

Su, X.

2017-12-01

A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

Workshop on b-phenomenology, Edinburgh, UK, 8-14 December 1991

International Nuclear Information System (INIS)

Clarke, P.E.L.; Peach, K.J.; Richards, D.G.

1992-01-01

The frontier of understanding in heavy quark theory is moving fast. With the data emerging from LEP and from hadron facilities on heavy hadrons, and the developing interest in a custom-built B-factory, there is a need for detailed interaction among experimentalists and phenomenologically motivated theorists on the physics of b-quarks and B hadrons. To this end a workshop on heavy flavour physics was held at the University of Edinburgh from 8-14 December 1991. (Author)

{sup 18}F-NaF PET/CT: EANM procedure guidelines for bone imaging

Energy Technology Data Exchange (ETDEWEB)

Beheshti, M.; Langsteger, W. [St Vincent' s Hospital, PET - CT Center LINZ, Department of Nuclear Medicine and Endocrinology, Linz (Austria); Mottaghy, F.M. [University Hospital Aachen, RWTH Aachen University, Department of Nuclear Medicine, Aachen (Germany); Maastricht University Medical Center, Department of Nuclear Medicine, Maastricht (Netherlands); Payche, F. [Louis Mourier Hospital, Department of Nuclear Medicine, Colombes (France); Behrendt, F.F.F. [University Hospital Aachen, RWTH Aachen University, Department of Nuclear Medicine, Aachen (Germany); Wyngaert, T.V. den [Antwerp University Hospital, Department of Nuclear Medicine, Edegem (Belgium); Fogelman, I. [King' s College, Department of Nuclear Medicine, London (United Kingdom); Strobel, K. [Lucerne Cantonal Hospital, Department of Radiology and Nuclear Medicine, Lucerne (Switzerland); Celli, M.; Fanti, S. [Policlinico S. Orsola-Malpighi, Department of Nuclear Medicine, PET Unit, Bologna (Italy); Giammarile, F. [Centre Hospitalier Universitaire de Lyon, Department of Nuclear Medicine, Lyon (France); Krause, B. [University Hospital Rostock, Department of Nuclear Medicine, Rostock (Germany)

2015-10-15

The aim of this guideline is to provide minimum standards for the performance and interpretation of {sup 18}F-NaF PET/CT scans. Standard acquisition and interpretation of nuclear imaging modalities will help to provide consistent data acquisition and numeric values between different platforms and institutes and to promote the use of PET/CT modality as an established diagnostic modality in routine clinical practice. This will also improve the value of scientific work and its contribution to evidence-based medicine. (orig.)

Making beautiful deep-sky images astrophotography with affordable equipment and software

CERN Document Server

Parker, Greg

2017-01-01

In this updated version of his classic on deep-sky imaging, astrophotographer Greg Parker describes how the latest technology can help amateur astronomers process their own beautiful images. Whether you are taking your own images from a backyard system or processing data from space telescopes, this book shows you how to enhance the visuals in the "electronic darkroom" for maximum beauty and impact. The wealth of options in the astrophotography realm has exploded in the recent past, and Parker proves an able guide for the interested imager to improve his or her comfort level against this exciting new technological backdrop. From addressing the latest DSLR equipment to updating the usage of Hyperstar imaging telescopes and explaining the utility of parallel imaging arrays, this edition brings the book fully up-to-date, and includes clear tutorials, helpful references, and gorgeous color astrophotography by one of the experts in the field.

Flexible power 90W to 120W ArF immersion light source for future semiconductor lithography

Science.gov (United States)

Burdt, R.; Thornes, J.; Duffey, T.; Bibby, T.; Rokitski, R.; Mason, E.; Melchior, J.; Aggarwal, T.; Haran, D.; Wang, J.; Rechtsteiner, G.; Haviland, M.; Brown, D.

2014-03-01

Semiconductor market demand for improved performance at lower cost continues to drive enhancements in excimer light source technologies. Increased output power, reduced variability in key light source parameters, and improved beam stability are required of the light source to support immersion lithography, multi-patterning, and 450mm wafer applications in high volume semiconductor manufacturing. To support future scanner needs, Cymer conducted a technology demonstration program to evaluate the design elements for a 120W ArFi light source. The program was based on the 90W XLR 600ix platform, and included rapid power switching between 90W and 120W modes to potentially support lot-to-lot changes in desired power. The 120W requirements also included improved beam stability in an exposure window conditionally reduced by 20%. The 120W output power is achieved by efficiency gains in system design, keeping system input power at the same level as the 90W XLR 600ix. To assess system to system variability, detailed system testing was conducted from 90W - 120W with reproducible results.

Evaluation of 18F-labeled icotinib derivatives as potential PET agents for tumor imaging

International Nuclear Information System (INIS)

Hongyu Ren; Hongyu Ning; Jin Chang; Mingxia Zhao; Yong He; Yan Chong; Chuanmin Qi

2016-01-01

In this study, three 18 F-labeled crown ether fused anilinoquinazoline derivatives ([ 18 F]11a-c) were synthesized and evaluated as potential tumor imaging probes. The biodistribution results of [ 18 F]11b were good. Compared with [ 18 F]-fludeoxyglucose and l-[ 18 F]-fluoroethyltyrosine in the same animal model, [ 18 F]11b had better tumor/brain, tumor/muscle, and tumor/blood uptake ratios. Overall, these results suggest that [ 18 F]11b is promising as a tumor imaging agent for positron emission tomography. (author)

Anesthesia condition for 18F-FDG imaging of lung metastasis tumors using small animal PET

International Nuclear Information System (INIS)

Woo, Sang-Keun; Lee, Tae Sup; Kim, Kyeong Min; Kim, June-Youp; Jung, Jae Ho; Kang, Joo Hyun; Cheon, Gi Jeong; Choi, Chang Woon; Lim, Sang Moo

2008-01-01

Small animal positron emission tomography (PET) with 18 F-FDG has been increasingly used for tumor imaging in the murine model. The aim of this study was to establish the anesthesia condition for imaging of lung metastasis tumor using small animal 18 F-FDG PET. Methods: To determine the impact of anesthesia on 18 F-FDG distribution in normal mice, five groups were studied under the following conditions: no anesthesia, ketamine and xylazine (Ke/Xy), 0.5% isoflurane (Iso 0.5), 1% isoflurane (Iso 1) and 2% isoflurane (Iso 2). The ex vivo counting, standard uptake value (SUV) image and glucose SUV of 18 F-FDG in various tissues were evaluated. The 18 F-FDG images in the lung metastasis tumor model were obtained under no anesthesia, Ke/Xy and Iso 0.5, and registered with CT image to clarify the tumor region. Results: Blood glucose concentration and muscle uptake of 18 F-FDG in the Ke/Xy group markedly increased more than in the other groups. The Iso 2 group increased 18 F-FDG uptake in heart compared with the other groups. The Iso 0.5 anesthesized group showed the lowest 18 F-FDG uptake in heart and chest wall. The small size of lung metastasis tumor (2 mm) was clearly visualized by 18 F-FDG image with the Iso 0.5 anesthesia. Conclusion: Small animal 18 F-FDG PET imaging with Iso 0.5 anesthesia was appropriate for the detection of lung metastasis tumor. To acquire 18 F-FDG PET images with small animal PET, the type and level of anesthetic should be carefully considered to be suitable for the visualization of target tissue in the experimental model

(18)F-nanobody for PET imaging of HER2 overexpressing tumors.

Science.gov (United States)

Xavier, Catarina; Blykers, Anneleen; Vaneycken, Ilse; D'Huyvetter, Matthias; Heemskerk, Jan; Lahoutte, Tony; Devoogdt, Nick; Caveliers, Vicky

2016-04-01

Radiolabeled nanobodies are exciting new probes for molecular imaging due to high affinity, high specificity and fast washout from the blood. Here we present the labeling of an anti-HER2 nanobody with (18)F and its validation for in vivo assessment of HER2 overexpression. The GMP grade anti-HER2 nanobody was labeled with the prosthetic group, N-succinimidyl-4-[(18)F]fluorobenzoate ([(18)F]-SFB), and its biodistribution, tumor targeting and specificity were evaluated in mouse and rat tumor models. [(18)F]FB-anti-HER2 nanobody was prepared with a 5-15% global yield (decay corrected) and a specific activity of 24.7 ± 8.2 MBq/nmol. In vivo studies demonstrated a high specific uptake for HER2 positive xenografts (5.94 ± 1.17 and 3.74 ± 0.52%IA/g, 1 and 3h p.i.) with high tumor-to-blood and tumor-to-muscle ratios generating high contrast PET imaging. The probe presented fast clearance through the kidneys (4%IA/g at 3h p.i.). [(18)F]FB-anti-HER2 nanobody is able to image HER2 expressing tumors when co-administered with the anti-HER2 therapeutic antibody trastuzumab (Herceptin), indicating the possibility of using the tracer in patients undergoing Herceptin therapy. The GMP grade anti-HER2 nanobody was labeled with (18)F. This new PET probe for imaging HER2 overexpression in tumors has ample potential for clinical translation. Copyright © 2016 Elsevier Inc. All rights reserved.

18F-nanobody for PET imaging of HER2 overexpressing tumors

International Nuclear Information System (INIS)

Xavier, Catarina; Blykers, Anneleen; Vaneycken, Ilse; D'Huyvetter, Matthias; Heemskerk, Jan; Lahoutte, Tony; Devoogdt, Nick; Caveliers, Vicky

2016-01-01

Introduction: Radiolabeled nanobodies are exciting new probes for molecular imaging due to high affinity, high specificity and fast washout from the blood. Here we present the labeling of an anti-HER2 nanobody with 18 F and its validation for in vivo assessment of HER2 overexpression. Methods: The GMP grade anti-HER2 nanobody was labeled with the prosthetic group, N-succinimidyl-4-[ 18 F]fluorobenzoate ([ 18 F]-SFB), and its biodistribution, tumor targeting and specificity were evaluated in mouse and rat tumor models. Results: [ 18 F]FB-anti-HER2 nanobody was prepared with a 5–15% global yield (decay corrected) and a specific activity of 24.7 ± 8.2 MBq/nmol. In vivo studies demonstrated a high specific uptake for HER2 positive xenografts (5.94 ± 1.17 and 3.74 ± 0.52%IA/g, 1 and 3 h p.i.) with high tumor-to-blood and tumor-to-muscle ratios generating high contrast PET imaging. The probe presented fast clearance through the kidneys (4%IA/g at 3 h p.i.). [ 18 F]FB-anti-HER2 nanobody is able to image HER2 expressing tumors when co-administered with the anti-HER2 therapeutic antibody trastuzumab (Herceptin), indicating the possibility of using the tracer in patients undergoing Herceptin therapy. Conclusions: The GMP grade anti-HER2 nanobody was labeled with 18 F. This new PET probe for imaging HER2 overexpression in tumors has ample potential for clinical translation.

18F-labelled annexin V: a PET tracer for apoptosis imaging

International Nuclear Information System (INIS)

Murakami, Yoshihiro; Tatsumi, Mitsuyoshi; Ichise, Rikiya; Nishimura, Shintaro; Takamatsu, Hiroyuki; Noda, Akihiro; Taki, Junichi; Tait, Jonathan F.

2004-01-01

Annexin V can be used to detect apoptotic cells in vitro and in vivo, based on its ability to identify extracellular phosphatidylserine, which arises during apoptosis. In the present study, we examined the synthesis of fluorine-18 labelled annexin V as a positron emission tomography tracer for apoptosis imaging. The distribution of [ 18 F]annexin V and technetium-99m labelled annexin V, a well-characterised SPET tracer for apoptosis imaging, was compared. [ 18 F]annexin V was synthesised using N-succinimidyl 4-[ 18 F]fluorobenzoate as an 18 F labelling reagent. Synthesised and purified [ 18 F]annexin V was confirmed by SDS-PAGE. In an ex vivo imaging experiment, [ 18 F]annexin V was intravenously injected into rats 24 h after the induction of myocardial ischaemia, and accumulation in the left ventricle was examined. [ 18 F]annexin V accumulated in the infarct area of the left ventricle, where apoptotic cells were observed. In separate experiments, [ 18 F]annexin V or [ 99m Tc]annexin V was intravenously injected into ischaemic or normal animals, and the distribution of the tracers was compared. In ischaemic animals, accumulation of [ 18 F]annexin V and [ 99m Tc]annexin V in the infarct area was about threefold higher than in the non-infarct area. Furthermore, the ratio of accumulation in the normal heart to the blood radioactivity was not significantly different between the tracers. In normal animals, however, the uptake of [ 18 F]annexin V in the liver, spleen and kidney was much lower than that of [ 99m Tc]annexin V. The low uptake of [ 18 F]annexin V in these organs might represent an advantage over [ 99m Tc]annexin V. (orig.)

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

Science.gov (United States)

Mezgec, Simon; Koroušić Seljak, Barbara

2017-06-27

Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an accuracy of 94 . 47 % on a detection dataset containing 130 , 517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson's disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 % , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson's disease patients.

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer

Science.gov (United States)

Weng, Sheng; Xu, Xiaoyun; Li, Jiasong; Wong, Stephen T. C.

2017-10-01

Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging.

Representation learning with deep extreme learning machines for efficient image set classification

KAUST Repository

Uzair, Muhammad

2016-12-09

Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

Representation learning with deep extreme learning machines for efficient image set classification

KAUST Repository

Uzair, Muhammad; Shafait, Faisal; Ghanem, Bernard; Mian, Ajmal

2016-01-01

Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

Comparative study of deep learning methods for one-shot image classification (abstract)

NARCIS (Netherlands)

van den Bogaert, J.; Mohseni, H.; Khodier, M.; Stoyanov, Y.; Mocanu, D.C.; Menkovski, V.

2017-01-01

Training deep learning models for images classification requires large amount of labeled data to overcome the challenges of overfitting and underfitting. Usually, in many practical applications, these labeled data are not available. In an attempt to solve this problem, the one-shot learning paradigm