Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning

Sixing Yin; Yameng Han; Shufang Li

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arXiv · 2106.04127

Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning

Sixing Yin·Yameng Han·Shufang Li

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Abstract

Medical image segmentation is one of the important tasks of computer-aided diagnosis in medical image analysis. Since most medical images have the characteristics of blurred boundaries and uneven intensity distribution, through existing segmentation methods, the discontinuity within the target area and the discontinuity of the target boundary are likely to lead to rough or even erroneous boundary delineation. In this paper, we propose a new iterative refined interactive segmentation method for medical images based on agent reinforcement learning, which focuses on the problem of target segmentation boundaries. We model the dynamic process of drawing the target contour in a certain order as a Markov Decision Process (MDP) based on a deep reinforcement learning method. In the dynamic process of continuous interaction between the agent and the image, the agent tracks the boundary point by point in order within a limited length range until the contour of the target is completely drawn. In this process, the agent can quickly improve the segmentation performance by exploring an interactive policy in the image. The method we proposed is simple and effective. At the same time, we evaluate our method on the cardiac MRI scan data set. Experimental results show that our method has a better segmentation effect on the left ventricle in a small number of medical image data sets, especially in terms of segmentation boundaries, this method is better than existing methods. Based on our proposed method, the dynamic generation process of the predicted contour trajectory of the left ventricle will be displayed online at https://github.com/H1997ym/LV-contour-trajectory.

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BibTeX RIS

Sixing Yin, Yameng Han, Shufang Li. 2021-06-08. Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning. https://arxiv.org/abs/2106.04127

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