title: A comprehensive non-invasive framework for diagnosing prostate cancer.
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.
authors.affiliation: Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; Bioengineering Department, University of Louisville, Louisville KY 40292, USA.
authors.forename: Islam
authors.initials: I
authors.lastname: Reda
authors.affiliation: Bioengineering Department, University of Louisville, Louisville KY 40292, USA.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Shalaby
authors.affiliation: Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; Bioengineering Department, University of Louisville, Louisville KY 40292, USA.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Elmogy
authors.affiliation: Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.
authors.forename: Ahmed Abou
authors.initials: AA
authors.lastname: Elfotouh
authors.affiliation: Bioengineering Department, University of Louisville, Louisville KY 40292, USA; Electronics and Communication Engineering Department, Mansoura University, Mansoura, Egypt.
authors.forename: Fahmi
authors.initials: F
authors.lastname: Khalifa
authors.affiliation: Radiology Department, Urology and Nephrology Center, University of Mansoura, Egypt.
authors.forename: Mohamed Abou
authors.initials: MA
authors.lastname: El-Ghar
authors.affiliation: Electrical and Computer Engineering, University of Louisville, Louisville KY 40292, USA.
authors.forename: Ehsan
authors.initials: E
authors.lastname: Hosseini-Asl
authors.affiliation: Department of Computer Science, University of Auckland, Auckland, New Zealand.
authors.forename: Georgy
authors.initials: G
authors.lastname: Gimel'farb
authors.affiliation: Khalifa University of Science Technology and Research, Abu Dhabi, UAE.
authors.forename: Naoufel
authors.initials: N
authors.lastname: Werghi
authors.affiliation: Bioengineering Department, University of Louisville, Louisville KY 40292, USA. Electronic address: aselba01@louisville.edu.
authors.forename: Ayman
authors.initials: A
authors.lastname: El-Baz
mesh_terms: D000465:Algorithms; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D055088:Early Detection of Cancer / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: CAD; DW-MRI; MGRF; NMF; Prostate cancer