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Gen Li

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Proceedings Article | 16 March 2020 Paper

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

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

Proceedings Volume 11315, 113152R (2020) https://doi.org/10.1117/12.2550448

KEYWORDS: Image segmentation, Prostate, Medical imaging, Magnetic resonance imaging, Neural networks, Convolution

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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.

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