Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning release_lgrr7s7kczdc5enthydpyh2exm

by Jingjing XIONG, Lai Man Po, Kwok Wai Cheung, Pengfei Xian, Yuzhi Zhao, yasar rehman, Zhang Yujia

Published in Sensors by MDPI AG.

2021   Volume 21, Issue 7, p2375

Abstract

Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.
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Date   2021-03-29
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DOI  10.3390/s21072375
PubMed  33805558
PMC  PMC8037138
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