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Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention: International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Pro...

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Personen und Körperschaften: Madabhushi, Anant (VerfasserIn), Dowling, Jason (Sonstige), Yan, Pingkun (Sonstige), Fenster, Aaron (Sonstige), Abolmaesumi, Purang (Sonstige), Hata, Nobuhiko (Sonstige)
Titel: Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention: International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings/ edited by Anant Madabhushi, Jason Dowling, Pingkun Yan, Aaron Fenster, Purang Abolmaesumi, Nobuhiko Hata
Format: E-Book Konferenzbericht
Sprache: Englisch
veröffentlicht:
Berlin, Heidelberg Springer Berlin Heidelberg 2010
Gesamtaufnahme: SpringerLink
Lecture notes in computer science ; 6367
Schlagwörter:
Computer science
Optical pattern recognition
Computer Science
Computer simulation
Computer vision
Computer graphics
Pattern recognition systems.
Human-computer interaction.
Image processing
User interfaces (Computer systems).
Konferenzschrift > 2010 > Peking
Buchausg. u.d.T.: Prostate cancer imaging, Berlin : Springer, 2010, X, 144 S.
Quelle: Verbunddaten SWB
Zugangsinformationen: Elektronischer Volltext - Campuslizenz
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contents Prostate Cancer MR Imaging -- Computer Aided Detection of Prostate Cancer Using T2, DWI and DCE MRI: Methods and Clinical Applications -- Prostate Cancer Segmentation Using Multispectral Random Walks -- Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer -- An Efficient Inverse-Consistent Diffeomorphic Image Registration Method for Prostate Adaptive Radiotherapy -- Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy -- Realtime TRUS/MRI Fusion Targeted-Biopsy for Prostate Cancer: A Clinical Demonstration of Increased Positive Biopsy Rates -- HistoCAD: Machine Facilitated Quantitative Histoimaging with Computer Assisted Diagnosis -- Registration of In Vivo Prostate Magnetic Resonance Images to Digital Histopathology Images -- High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies -- Automated Analysis of PIN-4 Stained Prostate Needle Biopsies -- Augmented Reality Image Guidance in Minimally Invasive Prostatectomy -- Texture Guided Active Appearance Model Propagation for Prostate Segmentation -- Novel Stochastic Framework for Accurate Segmentation of Prostate in Dynamic Contrast Enhanced MRI -- Boundary Delineation in Prostate Imaging Using Active Contour Segmentation Method with Interactively Defined Object Regions., Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images.
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spelling Madabhushi, Anant aut, Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings edited by Anant Madabhushi, Jason Dowling, Pingkun Yan, Aaron Fenster, Purang Abolmaesumi, Nobuhiko Hata, Berlin, Heidelberg Springer Berlin Heidelberg 2010, Online-Ressource (X, 146p. 67 illus, digital), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Lecture Notes in Computer Science 6367, SpringerLink Bücher, Prostate Cancer MR Imaging -- Computer Aided Detection of Prostate Cancer Using T2, DWI and DCE MRI: Methods and Clinical Applications -- Prostate Cancer Segmentation Using Multispectral Random Walks -- Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer -- An Efficient Inverse-Consistent Diffeomorphic Image Registration Method for Prostate Adaptive Radiotherapy -- Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy -- Realtime TRUS/MRI Fusion Targeted-Biopsy for Prostate Cancer: A Clinical Demonstration of Increased Positive Biopsy Rates -- HistoCAD: Machine Facilitated Quantitative Histoimaging with Computer Assisted Diagnosis -- Registration of In Vivo Prostate Magnetic Resonance Images to Digital Histopathology Images -- High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies -- Automated Analysis of PIN-4 Stained Prostate Needle Biopsies -- Augmented Reality Image Guidance in Minimally Invasive Prostatectomy -- Texture Guided Active Appearance Model Propagation for Prostate Segmentation -- Novel Stochastic Framework for Accurate Segmentation of Prostate in Dynamic Contrast Enhanced MRI -- Boundary Delineation in Prostate Imaging Using Active Contour Segmentation Method with Interactively Defined Object Regions., Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images., Computer science, Optical pattern recognition, Computer Science, Computer simulation, Computer vision, Computer graphics, Pattern recognition systems., Human-computer interaction., Image processing, User interfaces (Computer systems)., Konferenzschrift 2010 Peking (DE-588)1071861417 (DE-627)826484824 (DE-576)433375485 gnd-content, s (DE-588)4047511-6 (DE-627)106194399 (DE-576)209073799 Prostatakrebs gnd, s (DE-588)4006617-4 (DE-627)104409800 (DE-576)208866329 Bildgebendes Verfahren gnd, s (DE-588)4139030-1 (DE-627)104369949 (DE-576)209691026 Computerunterstütztes Verfahren gnd, DE-101, Dowling, Jason oth, Yan, Pingkun oth, Fenster, Aaron oth, Abolmaesumi, Purang oth, Hata, Nobuhiko oth, 9783642159886, Buchausg. u.d.T. Prostate cancer imaging Berlin : Springer, 2010 X, 144 S. (DE-627)63476795X (DE-576)330699636 3642159885 9783642159886, Lecture notes in computer science 6367 6367 (DE-627)316228877 (DE-576)093890923 (DE-600)2018930-8 1611-3349 ns, https://doi.org/10.1007/978-3-642-15989-3 X:SPRINGER Verlag lizenzpflichtig Volltext, http://dx.doi.org/10.1007/978-3-642-15989-3 DE-14, DE-14 2011-07-20T14:58:26Z epn:3336213541, http://dx.doi.org/10.1007/978-3-642-15989-3 DE-15, DE-15 2011-05-16T16:34:39Z epn:3336213606, http://dx.doi.org/10.1007/978-3-642-15989-3 Zum Online-Dokument DE-Zi4, DE-Zi4 2011-01-26T14:29:00Z epn:3336213703, http://dx.doi.org/10.1007/978-3-642-15989-3 DE-520, DE-520 2010-10-05T14:43:41Z epn:3336213754
spellingShingle Madabhushi, Anant, Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention: International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings, Lecture notes in computer science, 6367, Prostate Cancer MR Imaging -- Computer Aided Detection of Prostate Cancer Using T2, DWI and DCE MRI: Methods and Clinical Applications -- Prostate Cancer Segmentation Using Multispectral Random Walks -- Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer -- An Efficient Inverse-Consistent Diffeomorphic Image Registration Method for Prostate Adaptive Radiotherapy -- Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy -- Realtime TRUS/MRI Fusion Targeted-Biopsy for Prostate Cancer: A Clinical Demonstration of Increased Positive Biopsy Rates -- HistoCAD: Machine Facilitated Quantitative Histoimaging with Computer Assisted Diagnosis -- Registration of In Vivo Prostate Magnetic Resonance Images to Digital Histopathology Images -- High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies -- Automated Analysis of PIN-4 Stained Prostate Needle Biopsies -- Augmented Reality Image Guidance in Minimally Invasive Prostatectomy -- Texture Guided Active Appearance Model Propagation for Prostate Segmentation -- Novel Stochastic Framework for Accurate Segmentation of Prostate in Dynamic Contrast Enhanced MRI -- Boundary Delineation in Prostate Imaging Using Active Contour Segmentation Method with Interactively Defined Object Regions., Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images., Computer science, Optical pattern recognition, Computer Science, Computer simulation, Computer vision, Computer graphics, Pattern recognition systems., Human-computer interaction., Image processing, User interfaces (Computer systems)., Konferenzschrift 2010 Peking, Prostatakrebs, Bildgebendes Verfahren, Computerunterstütztes Verfahren
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title Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention: International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings
title_auth Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings
title_full Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings edited by Anant Madabhushi, Jason Dowling, Pingkun Yan, Aaron Fenster, Purang Abolmaesumi, Nobuhiko Hata
title_fullStr Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings edited by Anant Madabhushi, Jason Dowling, Pingkun Yan, Aaron Fenster, Purang Abolmaesumi, Nobuhiko Hata
title_full_unstemmed Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings edited by Anant Madabhushi, Jason Dowling, Pingkun Yan, Aaron Fenster, Purang Abolmaesumi, Nobuhiko Hata
title_in_hierarchy 6367. Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention: International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings (2010)
title_short Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention
title_sort prostate cancer imaging computer aided diagnosis prognosis and intervention international workshop held in conjunction with miccai 2010 beijing china september 24 2010 proceedings
title_sub International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings
title_unstemmed Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention: International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings
topic Computer science, Optical pattern recognition, Computer Science, Computer simulation, Computer vision, Computer graphics, Pattern recognition systems., Human-computer interaction., Image processing, User interfaces (Computer systems)., Konferenzschrift 2010 Peking, Prostatakrebs, Bildgebendes Verfahren, Computerunterstütztes Verfahren
topic_facet Computer science, Optical pattern recognition, Computer Science, Computer simulation, Computer vision, Computer graphics, Pattern recognition systems., Human-computer interaction., Image processing, User interfaces (Computer systems)., Konferenzschrift, Prostatakrebs, Bildgebendes Verfahren, Computerunterstütztes Verfahren
url https://doi.org/10.1007/978-3-642-15989-3, http://dx.doi.org/10.1007/978-3-642-15989-3