TY - BOOK ID - 8435214 TI - Prostate cancer imaging : computer-aided diagnosis, prognosis, and intervention : international workshop held in conjunction with MICCAI 2010, Beijing, China, September 24, 2010 : proceedings AU - Madabhushi, Anant. AU - Prostate Cancer Imaging Workshop PY - 2010 SN - 3642159885 9786613567246 3642159893 128038932X PB - Berlin ; New York : Springer, DB - UniCat KW - Prostate KW - Image-guided radiation therapy KW - Diagnostic Imaging KW - Genital Neoplasms, Male KW - Publication Formats KW - Decision Making, Computer-Assisted KW - Diagnosis, Computer-Assisted KW - Therapeutics KW - Analytical, Diagnostic and Therapeutic Techniques and Equipment KW - Prostatic Diseases KW - Genital Diseases, Male KW - Urogenital Neoplasms KW - Diagnostic Techniques and Procedures KW - Publication Characteristics KW - Medical Informatics Applications KW - Male Urogenital Diseases KW - Neoplasms by Site KW - Medical Informatics KW - Diseases KW - Neoplasms KW - Information Science KW - Image Interpretation, Computer-Assisted KW - Prostatic Neoplasms KW - Congresses KW - Prognosis KW - Therapy, Computer-Assisted KW - Diagnosis KW - Medicine KW - Engineering & Applied Sciences KW - Health & Biological Sciences KW - Oncology KW - Applied Physics KW - Cancer KW - Radiotherapy KW - IGRT (Image-guided radiation therapy) KW - Image-guided radiotherapy KW - Computer science. KW - User interfaces (Computer systems). KW - Computer simulation. KW - Computer graphics. KW - Image processing. KW - Pattern recognition. KW - Computer Science. KW - User Interfaces and Human Computer Interaction. KW - Image Processing and Computer Vision. KW - Pattern Recognition. KW - Computer Graphics. KW - Computer Imaging, Vision, Pattern Recognition and Graphics. KW - Simulation and Modeling. KW - Design perception KW - Pattern recognition KW - Form perception KW - Perception KW - Figure-ground perception KW - Pictorial data processing KW - Picture processing KW - Processing, Image KW - Imaging systems KW - Optical data processing KW - Automatic drafting KW - Graphic data processing KW - Graphics, Computer KW - Computer art KW - Graphic arts KW - Electronic data processing KW - Engineering graphics KW - Image processing KW - Computer modeling KW - Computer models KW - Modeling, Computer KW - Models, Computer KW - Simulation, Computer KW - Electromechanical analogies KW - Mathematical models KW - Simulation methods KW - Model-integrated computing KW - Interfaces, User (Computer systems) KW - Human-machine systems KW - Human-computer interaction KW - Informatics KW - Science KW - Digital techniques KW - Diagnostic imaging KW - Computer vision. KW - Optical pattern recognition. KW - Pattern perception KW - Perceptrons KW - Visual discrimination KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Pattern recognition systems KW - Optical data processing. KW - Optical computing KW - Visual data processing KW - Bionics KW - Integrated optics KW - Photonics KW - Computers KW - Optical equipment KW - Peking <2010> UR - https://www.unicat.be/uniCat?func=search&query=sysid:8435214 AB - 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. ER -