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Proceedings Paper

Multi-step segmentation for prostate MR image based on reinforcement learning
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Paper Abstract

Medical image segmentation is a complex and critical step in the field of medical image processing and analysis. Manual annotation of the medical image requires a lot of effort by professionals, which is a subjective task. In recent years, researchers have proposed a number of models for automatic medical image segmentation. In this paper, we formulate the medical image segmentation problem as a Markov Decision Process (MDP) and optimize it by reinforcement learning method. The proposed medical image segmentation method mimics a professional delineating the foreground of medical images in a multi-step manner. The proposed model get notable accuracy compared to popular methods on prostate MR data sets. Meanwhile, we adopted a deep reinforcement learning (DRL) algorithm called deep deterministic policy gradient (DDPG) to learn the segmentation model, which provides an insight on medical image segmentation problem.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113152R (16 March 2020); doi: 10.1117/12.2550448
Show Author Affiliations
Xiangyu Si, Xi'an Jiaotong Univ. (China)
Zhiqiang Tian, Xi'an Jiaotong Univ. (China)
Xiaojian Li, Xi'an Jiaotong Univ. (China)
Zhang Chen, Xi'an Jiaotong Univ. (China)
Gen Li, Xi'an Jiaotong Univ. (China)
James D. Dormer, The Univ. of Texas at Dallas (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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