Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching
Yanrong Guo; Yaozong Gao; Dinggang Shen University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
Abstract
Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (i) inhomogeneous and inconsistent appearances around the prostate boundary, and (ii) large variation of the prostate shapes across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with ...
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