FISMI-DRL: A Framework for Interactive Segmentation of Medical Image Based On Deep Reinforcement Learning

Abstract

At present, deep learning-based medical image segmentation algorithms have achieved fast and accurate semantic segmentation. However, their segmentation accuracy is still challenging to reach the clinical use standard, requiring further refinement by medical experts. Therefore, some researchers have turned their attention to interactive segmentation methods, which introduce human interaction to obtain information gain. Such methods model the dynamics of the image annotation process state and can effectively improve the segmentation accuracy under the interaction of medical experts. In this paper, we put forward a novel framework for the interactive segmentation of medical images based on deep reinforcement learning, namely FISMI-DRL. The experimental results demonstrate that our model achieves high segmentation accuracy and interaction efficiency.

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