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.
Author(s): Jurgen J. Fütterer (auth.), Anant Madabhushi, Jason Dowling, Pingkun Yan, Aaron Fenster, Purang Abolmaesumi, Nobuhiko Hata (eds.)
Series: Lecture Notes in Computer Science 6367 : Image Processing, Computer Vision, Pattern Recognition, and Graphics
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2010
Language: English
Pages: 146
Tags: Image Processing and Computer Vision; Pattern Recognition; User Interfaces and Human Computer Interaction; Computer Graphics; Computer Imaging, Vision, Pattern Recognition and Graphics; Simulation and Modeling
Front Matter....Pages -
Prostate Cancer MR Imaging....Pages 1-3
Computer Aided Detection of Prostate Cancer Using T2, DWI and DCE MRI: Methods and Clinical Applications....Pages 4-14
Prostate Cancer Segmentation Using Multispectral Random Walks....Pages 15-24
Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer....Pages 25-33
An Efficient Inverse-Consistent Diffeomorphic Image Registration Method for Prostate Adaptive Radiotherapy....Pages 34-41
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....Pages 42-51
Realtime TRUS/MRI Fusion Targeted-Biopsy for Prostate Cancer: A Clinical Demonstration of Increased Positive Biopsy Rates....Pages 52-62
HistoCAD: Machine Facilitated Quantitative Histoimaging with Computer Assisted Diagnosis....Pages 63-65
Registration of In Vivo Prostate Magnetic Resonance Images to Digital Histopathology Images....Pages 66-76
High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies....Pages 77-88
Automated Analysis of PIN-4 Stained Prostate Needle Biopsies....Pages 89-100
Augmented Reality Image Guidance in Minimally Invasive Prostatectomy....Pages 101-110
Texture Guided Active Appearance Model Propagation for Prostate Segmentation....Pages 111-120
Novel Stochastic Framework for Accurate Segmentation of Prostate in Dynamic Contrast Enhanced MRI....Pages 121-130
Boundary Delineation in Prostate Imaging Using Active Contour Segmentation Method with Interactively Defined Object Regions....Pages 131-142
Back Matter....Pages -
