Detection of benign prostatic hyperplasia nodules in T2W MR images using fuzzy decision forest

Nathan Lay; Sabrina Freeman; Baris Turkbey; Ronald M. Summers

doi:10.1117/12.2217906

24 March 2016 Detection of benign prostatic hyperplasia nodules in T2W MR images using fuzzy decision forest

Nathan Lay, Sabrina Freeman, Baris Turkbey, Ronald M. Summers

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 978527 (2016) https://doi.org/10.1117/12.2217906
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Nathan Lay, Sabrina Freeman, Baris Turkbey, and 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); https://doi.org/10.1117/12.2217906

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