MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning

Yang, Feiyang; Li, Xiongfei; Duan, Haoran; Xu, Feilong; Huang, Yawen; Zhang, Xiaoli; Long, Yang; Zheng, Yefeng

Abstract

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations across different slices. This presents a significant hurdle to medical image segmentation, as conventional segmentation methods may either overlook the minority class or overly emphasize the majority class, ultimately leading to a decrease in the overall generalization ability of the segmentation results. To overcome this, we propose a novel approach based on multi-step reinforcement learning, which integrates prior knowledge of medical images and pixel-wise segmentation difficulty into the reward function. Our method treats each pixel as an individual agent, utilizing diverse actions to evaluate its relevance for segmentation. To validate the effectiveness of our approach, we conduct experiments on four imbalanced medical datasets, and the results show that our approach surpasses other state-of-the-art methods in highly imbalanced scenarios. These findings hold substantial implications for clinical diagnosis and research.

Citation

Yang, F., Li, X., Duan, H., Xu, F., Huang, Y., Zhang, X., …Zheng, Y. (2024). MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning. IEEE Journal of Biomedical and Health Informatics, 28(2), 858-869. https://doi.org/10.1109/jbhi.2023.3336726

• Journal Article Type: Article
• Acceptance Date: Oct 1, 2023
• Online Publication Date: Nov 30, 2023
• Publication Date: 2024-02
• Deposit Date: Apr 19, 2024
• Publicly Available Date: Apr 19, 2024
• Journal: IEEE Journal of Biomedical and Health Informatics
• Print ISSN: 2168-2194
• Electronic ISSN: 2168-2208
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 28
• Issue: 2
• Pages: 858-869
• DOI: https://doi.org/10.1109/jbhi.2023.3336726
• Keywords: Health Information Management; Electrical and Electronic Engineering; Computer Science Applications; Health Informatics
• Public URL: https://durham-repository.worktribe.com/output/2388730