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Computers in biology and medicine v.81, 2017년, pp.148 - 158  

A comprehensive non-invasive framework for diagnosing prostate cancer

Reda, I. ; Shalaby, A. ; Elmogy, M. ; Elfotouh, A.A. ; Khalifa, F. ; El-Ghar, M.A. ; Hosseini-Asl, E. ; Gimel'farb, G. ; Werghi, N. ; El-Baz, A. ;
  • 초록  

    Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.


  • 주제어

    Prostate cancer .   DW-MRI .   NMF .   MGRF .   CAD.  

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