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

Xiangyu Si; Zhiqiang Tian; Xiaojian Li; Zhang Chen; Gen Li; James D. Dormer

doi:10.1117/12.2550448

16 March 2020 Multi-step segmentation for prostate MR image based on reinforcement learning

Xiangyu Si, Zhiqiang Tian, Xiaojian Li, Zhang Chen, Gen Li, James D. Dormer

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113152R (2020) https://doi.org/10.1117/12.2550448
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Citation Download Citation

Xiangyu Si, Zhiqiang Tian, Xiaojian Li, Zhang Chen, Gen Li, and James D. Dormer "Multi-step segmentation for prostate MR image based on reinforcement learning", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113152R (16 March 2020); https://doi.org/10.1117/12.2550448

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