24 March 2016 Detection of benign prostatic hyperplasia nodules in T2W MR images using fuzzy decision forest
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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.
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Nathan Lay, Nathan Lay, Sabrina Freeman, Sabrina Freeman, Baris Turkbey, Baris Turkbey, Ronald M. Summers, Ronald M. Summers, } "Detection of benign prostatic hyperplasia nodules in T2W MR images using fuzzy decision forest", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978527 (24 March 2016); doi: 10.1117/12.2217906; https://doi.org/10.1117/12.2217906
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