A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In Vivo Prostate DCE-MRI

  • Satish Viswanath
  • B. Nicolas Bloch
  • Elisabeth Genega
  • Neil Rofsky
  • Robert Lenkinski
  • Jonathan Chappelow
  • Robert Toth
  • Anant Madabhushi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)

Abstract

Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising modality for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function of contrast agent uptake by the lesion. Comparatively there is relatively little work in developing CAD schemes for prostate DCE-MRI. In this paper, we present a novel unsupervised detection scheme for CaP from 3 T DCE-MRI which comprises 3 distinct steps. First, a multi-attribute active shape model is used to automatically segment the prostate boundary from 3 T in vivo MR imagery. A robust multimodal registration scheme is then used to non-linearly align corresponding whole mount histological and DCE-MRI sections from prostatectomy specimens to determine the spatial extent of CaP. Non-linear dimensionality reduction schemes such as locally linear embedding (LLE) have been previously shown to be useful in projecting such high dimensional biomedical data into a lower dimensional subspace while preserving the non-linear geometry of the data manifold. DCE-MRI data is embedded via LLE and then classified via unsupervised consensus clustering to identify distinct classes. Quantitative evaluation on 21 histology-MRI slice pairs against registered CaP ground truth estimates yielded a maximum CaP detection accuracy of 77.20% while the popular three time point (3TP) scheme yielded an accuracy of 67.37%.

Keywords

Locally Linear Embedding Active Shape Model Consensus Cluster Nonlinear Dimensionality Reduction Contrast Agent Uptake 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Satish Viswanath
    • 1
  • B. Nicolas Bloch
    • 2
  • Elisabeth Genega
    • 2
  • Neil Rofsky
    • 2
  • Robert Lenkinski
    • 2
  • Jonathan Chappelow
    • 1
  • Robert Toth
    • 1
  • Anant Madabhushi
    • 1
  1. 1.Department of Biomedical EngineeringRutgers UniversityUSA
  2. 2.Department of Radiology, Beth Israel Deaconess Medical Center USA

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