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Proceedings Paper

Detection of benign prostatic hyperplasia nodules in T2W MR images using fuzzy decision forest
Author(s): Nathan Lay; Sabrina Freeman; Baris Turkbey; Ronald M. Summers
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Paper Abstract

Prostate cancer is the second leading cause of cancer-related death in men MRI has proven useful for detecting prostate cancer, and CAD may further improve detection. One source of false positives in prostate computer-aided diagnosis (CAD) is the presence of benign prostatic hyperplasia (BPH) nodules. These nodules have a distinct appearance with a pseudo-capsule on T2 weighted MR images but can also resemble cancerous lesions in other sequences such as the ADC or high B-value images. Describing their appearance with hand-crafted heuristics (features) that also exclude the appearance of cancerous lesions is challenging. This work develops a method based on fuzzy decision forests to automatically learn discriminative features for the purpose of BPH nodule detection in T2 weighted images for the purpose of improving prostate CAD systems.

Paper Details

Date Published: 24 March 2016
PDF: 8 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978527 (24 March 2016); doi: 10.1117/12.2217906
Show Author Affiliations
Nathan Lay, National Institutes of Health (United States)
Sabrina Freeman, National Institutes of Health (United States)
Baris Turkbey, National Institutes of Health (United States)
Ronald M. Summers, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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