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Proceedings Paper

Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images
Author(s): Yohannes K. Tsehay; Nathan S. Lay; Holger R. Roth; Xiaosong Wang; Jin Tae Kwak; Baris I. Turkbey; Peter A. Pinto; Brad J. Wood; Ronald M. Summers
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Paper Abstract

Prostate cancer (PCa) is the second most common cause of cancer related deaths in men. Multiparametric MRI (mpMRI) is the most accurate imaging method for PCa detection; however, it requires the expertise of experienced radiologists leading to inconsistency across readers of varying experience. To increase inter-reader agreement and sensitivity, we developed a computer-aided detection (CAD) system that can automatically detect lesions on mpMRI that readers can use as a reference. We investigated a convolutional neural network based deep-learing (DCNN) architecture to find an improved solution for PCa detection on mpMRI. We adopted a network architecture from a state-of-the-art edge detector that takes an image as an input and produces an image probability map. Two-fold cross validation along with a receiver operating characteristic (ROC) analysis and free-response ROC (FROC) were used to determine our deep-learning based prostate-CAD’s (CADDL) performance. The efficacy was compared to an existing prostate CAD system that is based on hand-crafted features, which was evaluated on the same test-set. CADDL had an 86% detection rate at 20% false-positive rate while the top-down learning CAD had 80% detection rate at the same false-positive rate, which translated to 94% and 85% detection rate at 10 false-positives per patient on the FROC. A CNN based CAD is able to detect cancerous lesions on mpMRI of the prostate with results comparable to an existing prostate-CAD showing potential for further development.

Paper Details

Date Published: 3 March 2017
PDF: 11 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013405 (3 March 2017); doi: 10.1117/12.2254423
Show Author Affiliations
Yohannes K. Tsehay, National Institutes of Health (United States)
Nathan S. Lay, National Institutes of Health (United States)
Holger R. Roth, National Institutes of Health (United States)
Xiaosong Wang, National Institutes of Health (United States)
Jin Tae Kwak, National Institutes of Health (United States)
Baris I. Turkbey, National Cancer Institute, National Institutes of Health (United States)
Peter A. Pinto, National Cancer Institute, National Institutes of Health (United States)
Brad J. Wood, National Cancer Institute, National Institutes of Health (United States)
Ronald M. Summers, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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