Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks
Author:
Affiliation:
1. University of Toronto, Department of Medical Imaging, Sunnybrook Research Institute, Toronto
2. University of Waterloo, Department of Systems Design Engineering, Waterloo
Publisher
SPIE-Intl Soc Optical Eng
Subject
Radiology Nuclear Medicine and imaging
Reference47 articles.
1. Prostate Cancer: Multiparametric MR Imaging for Detection, Localization, and Staging
2. Multiparametric Magnetic Resonance Imaging in the Management and Diagnosis of Prostate Cancer: Current Applications and Strategies
3. ESUR prostate MR guidelines 2012
4. Prostate Cancer: Interobserver Agreement and Accuracy with the Revised Prostate Imaging Reporting and Data System at Multiparametric MR Imaging
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