Multi-agent reinforcement learning for prostate localization based on multi-scale image representation

Chenyang Zheng; Xiangyu Si; Lei Sun; Zhang Chen; Linghao Yu; Zhiqiang Tian

doi:10.1117/12.2605920

28 October 2021 Multi-agent reinforcement learning for prostate localization based on multi-scale image representation

Chenyang Zheng, Xiangyu Si, Lei Sun, Zhang Chen, Linghao Yu, Zhiqiang Tian

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841G (2021) https://doi.org/10.1117/12.2605920
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

The analysis of magnetic resonance (MR) images plays an important role in medicine diagnosis. The localization of the anatomical structure of lesions or organs is a very important pretreatment step in clinical treatment planning. Furthermore, the accuracy of localization directly affects the diagnosis. We propose a multi-agent deep reinforcement learning-based method for prostate localization in MR image. We construct a collaborative communication environment for multi-agent interaction by sharing parameters of convolution layers of all agents. Because each agent needs to make action strategies independently, the fully connected layers are separate for each agent. In addition, we present a coarse-to-fine multi-scale image representation method to further improve the accuracy of prostate localization. The experimental results show that our method outperforms several state- of-the-art methods on PROMISE12 test dataset.

Citation Download Citation

Chenyang Zheng, Xiangyu Si, Lei Sun, Zhang Chen, Linghao Yu, and Zhiqiang Tian "Multi-agent reinforcement learning for prostate localization based on multi-scale image representation", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841G (28 October 2021); https://doi.org/10.1117/12.2605920

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KEYWORDS

Prostate

Magnetic resonance imaging

Convolution

Medical imaging

Detection and tracking algorithms

Machine learning

Network architectures

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