Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention

1. Front Matter

   PDF

2. Prostate Cancer MR Imaging

   • Jurgen J. Fütterer

   Pages 1-3

3. Computer Aided Detection of Prostate Cancer Using T2, DWI and DCE MRI: Methods and Clinical Applications

   • Henkjan Huisman, Pieter Vos, Geert Litjens, Thomas Hambrock, Jelle Barentsz

   Pages 4-14

4. Prostate Cancer Segmentation Using Multispectral Random Walks

   • Yusuf Artan, Masoom A. Haider, Imam Samil Yetik

   Pages 15-24

5. Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer

   • Jason Dowling, Jonathan Lambert, Joel Parker, Peter B. Greer, Jurgen Fripp, James Denham et al.

   Pages 25-33

6. An Efficient Inverse-Consistent Diffeomorphic Image Registration Method for Prostate Adaptive Radiotherapy

   • Xiao Han, Lyndon S. Hibbard, Virgil Willcut

   Pages 34-41

7. 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

   • Oscar Acosta, Jason Dowling, Guillaume Cazoulat, Antoine Simon, Olivier Salvado, Renaud de Crevoisier et al.

   Pages 42-51

8. Realtime TRUS/MRI Fusion Targeted-Biopsy for Prostate Cancer: A Clinical Demonstration of Increased Positive Biopsy Rates

   • Samuel Kadoury, Pingkun Yan, Sheng Xu, Neil Glossop, Peter Choyke, Baris Turkbey et al.

   Pages 52-62

9. HistoCAD: Machine Facilitated Quantitative Histoimaging with Computer Assisted Diagnosis

   • John E. Tomaszewski

   Pages 63-65

10. Registration of In Vivo Prostate Magnetic Resonance Images to Digital Histopathology Images

    • A. D. Ward, C. Crukley, C. McKenzie, J. Montreuil, E. Gibson, J. A. Gomez et al.

    Pages 66-76

11. High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies

    • Jun Xu, Rachel Sparks, Andrew Janowczyk, John E. Tomaszewski, Michael D. Feldman, Anant Madabhushi

    Pages 77-88

12. Automated Analysis of PIN-4 Stained Prostate Needle Biopsies

    • Bikash Sabata, Boris Babenko, Robert Monroe, Chukka Srinivas

    Pages 89-100

13. Augmented Reality Image Guidance in Minimally Invasive Prostatectomy

    • Daniel Cohen, Erik Mayer, Dongbin Chen, Ann Anstee, Justin Vale, Guang-Zhong Yang et al.

    Pages 101-110

14. Texture Guided Active Appearance Model Propagation for Prostate Segmentation

    • Soumya Ghose, Arnau Oliver, Robert Martí, Xavier Lladó, Jordi Freixenet, Joan C. Vilanova et al.

    Pages 111-120

15. Novel Stochastic Framework for Accurate Segmentation of Prostate in Dynamic Contrast Enhanced MRI

    • Ahmad Firjany, Ahmed Elnakib, Ayman El-Baz, Georgy Gimel’farb, Mohamed Abo El-Ghar, Adel Elmagharby

    Pages 121-130

16. Boundary Delineation in Prostate Imaging Using Active Contour Segmentation Method with Interactively Defined Object Regions

    • Yan Zhang, Bogdan J. Matuszewski, Aymeric Histace, Frédéric Precioso, Judith Kilgallon, Christopher Moore

    Pages 131-142

17. Back Matter

    PDF

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.

• Active Appearance Model
• Computer-Aided Design (CAD)
• HCI
• MRI
• augmented reality
• biopsy
• classification
• computer-aided diagnosis
• histoloy images
• image registration
• image retrieval
• image segmentation
• model
• morphology
• prostate imaging

• Department of Biomedical Engineering, Rutgers University, Piscataway, USA

  Anant Madabhushi

• CSIRO Australian e-Health Research Centre, Royal Brisbane and Women’s Hospital, Herston, Australia

  Jason Dowling

• Philips Research North America, Briarcliff Manor, USA

  Pingkun Yan

• Imaging Research Laboratories, Robarts Research Institute, London, Canada

  Aaron Fenster

• Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada

  Purang Abolmaesumi

• Brigham and Women’s Hospital and Harvard Medical School, St., Boston, USA

  Nobuhiko Hata

Policies and ethics