A comprehensive non-invasive framework for diagnosing prostate cancer. (1st February 2017)

• Record Type: Journal Article
• Authors: Reda, Islam; Shalaby, Ahmed; Elmogy, Mohammed; Elfotouh, Ahmed Abou; Khalifa, Fahmi; El-Ghar, Mohamed Abou; Hosseini-Asl, Ehsan; Gimel'farb, Georgy; Werghi, Naoufel; El-Baz, Ayman
• Is Part Of: Computers in biology and medicine. Volume 81(2017)
• Page Start: 148
• Page End: 158
• Publication Date: 2017-02-01
• Subjects: Prostate cancer -- DW-MRI -- NMF -- MGRF -- CAD; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.12.010
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Abstract : Highlights: The system extracts discriminative features to segment the prostate. NMF fuses image intensities, probabilistic shape prior, and spatial dependences. Segmented prostates are described with the integral statistics (CDFs of the ADCs). Benign or malignant cases are detected with the trained deep SNCAE classifier.