  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.
