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Self-Paced Learning for Automatic Prostate Segmentation on MR Images with Hierarchical Boundary Sensitive Network

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机构: [1]Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Guangdong, Peoples R China [2]City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China [3]Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China
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关键词: Hierarchical learning strategy boundary network branch residual U-net model prostate segmentation

摘要:
Accurate segmentation of Magnetic Resonance (MR) on prostate is an essential step for robotics surgery in prostate cancer treatment planning. This paper proposes a Hierarchical Boundary Sensitive Residual U-net (HBS-RUnet) model with self-paced learning strategy for prostate segmentation in MR image. Instead of regarding the segmentation task independently, our network consists of two branches: one segmentation branch detects the prostate region and the boundary branch finds prostate shape. The outputs of boundary branch are employed to refine the HBS-RUnet model by adding a boundary regularization, which helps to lind desirable and spatially consistent prostate region. Moreover, a hierarchical dynamic self-paced learning strategy is proposed to measure the difficulty for each prostate image and gradually select the relatively simpler samples for model training. Such a simple-to-complex learning strategy could robustly lean: image features and enable the robust prostate segmentation. We applied 66 cases from the PROSTATEx Challenge to evaluate the robustness and effectiveness of the proposed HBS-RUnet, and our fully automatic segmentation results demonstrate high consistency (DSC 87.1%) with the manual segmentation results by experienced physicians.

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第一作者机构: [1]Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Guangdong, Peoples R China
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