LEADER 06468nam 22008775 450 001 9910484587703321 005 20251226195813.0 010 $a1-280-38932-X 010 $a9786613567246 010 $a3-642-15989-3 024 7 $a10.1007/978-3-642-15989-3 035 $a(CKB)2670000000045129 035 $a(SSID)ssj0000446723 035 $a(PQKBManifestationID)11297685 035 $a(PQKBTitleCode)TC0000446723 035 $a(PQKBWorkID)10496849 035 $a(PQKB)10550298 035 $a(DE-He213)978-3-642-15989-3 035 $a(MiAaPQ)EBC3065827 035 $a(PPN)149025327 035 $a(EXLCZ)992670000000045129 100 $a20100914d2010 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aProstate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention $eInternational Workshop, Held in Conjunction with MICCAI 2010, Beijing, China, September 24, 2010, Proceedings /$fedited by Anant Madabhushi, Jason Dowling, Pingkun Yan, Aaron Fenster, Purang Abolmaesumi, Nobuhiko Hata 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (X, 146 p. 67 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v6367 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-642-15988-5 320 $aIncludes bibliographical references and index. 327 $aProstate 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. 330 $aProstatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v6367 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aComputer vision 606 $aPattern recognition systems 606 $aComputer graphics 606 $aImage processing$xDigital techniques 606 $aComputer simulation 606 $aUser Interfaces and Human Computer Interaction 606 $aComputer Vision 606 $aAutomated Pattern Recognition 606 $aComputer Graphics 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputer Modelling 615 0$aUser interfaces (Computer systems). 615 0$aHuman-computer interaction. 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 0$aComputer graphics. 615 0$aImage processing$xDigital techniques. 615 0$aComputer simulation. 615 14$aUser Interfaces and Human Computer Interaction. 615 24$aComputer Vision. 615 24$aAutomated Pattern Recognition. 615 24$aComputer Graphics. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputer Modelling. 676 $a005.437 676 $a4.019 686 $a610$2GyFmDB 701 $aMadabhushi$b Anant$01430861 712 12$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$d(13th :$f2010 :$eBeijing, China) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484587703321 996 $aProstate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention$94522596 997 $aUNINA