rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention
    sub title: International Workshop, Held in Conjunction with MICCAI 2010, Beijing,China, September 24, 2010. Proceedings
  name:
    name part: Madabhushi, Anant.
    role:
      role term: editor.
  name:
    name part: Dowling, Jason.
    role:
      role term: editor.
  name:
    name part: Yan, Pingkun.
    role:
      role term: editor.
  name:
    name part: Fenster, Aaron.
    role:
      role term: editor.
  name:
    name part: Abolmaesumi, Purang.
    role:
      role term: editor.
  name:
    name part: Hata, Nobuhiko.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 146 p. 67 illus. online resource.
  abstract: 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.
  table of 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.
  note: edited by Anant Madabhushi, Jason Dowling, Pingkun Yan, Aaron Fenster, Purang Abolmaesumi, Nobuhiko Hata.
  subject:
    topic: Computer science
  subject:
    topic: User interfaces (Computer systems)
  subject:
    topic: Computer simulation
  subject:
    topic: Computer graphics
  subject:
    topic: Image processing
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: User Interfaces and Human Computer Interaction
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Pattern Recognition
  subject:
    topic: Computer Graphics
  subject:
    topic: Computer Imaging, Vision, Pattern Recognition and Graphics
  subject:
    topic: Simulation and Modeling
  classification: QA76.9.U83
  classification: QA76.9.H85
  classification: 005.437
  classification: 4.019
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Computer Science, 6367
  identifier: 9783642159893
  identifier: http://dx.doi.org/10.1007/978-3-642-15989-3
  record info:
    record creation date: 100914
    record change date: 20151204165717.0
    record identifier: 978-3-642-15989-3