RCI-Seg: Robust click-based interactive segmentation framework with deep reinforcement learning for biomedical images

Recently, interactive segmentation models have achieved remarkable success in the field of biomedical images. However, these models rely on the accurate and high-quality interaction information provided by users, otherwise the segmentation performance will be seriously affected. This problem is more severe in multitarget biomedical images, which means that an image contains multiple targets of interest. It is extremely challenging for users to always maintain high-quality interactions. In this paper, we propose a novel two-stage segmentation model with robust interaction points for biomedical images. In the first stage, we implement robust interaction points based on user initial interaction points and the deep reinforcement learning (DRL) model. Specifically, we build a reinforcement learning environment to simulate the movement of interaction points with agents, and obtain improved interaction points (clue points) that are beneficial for segmentation. In the second stage, we use a convolutional neural network (CNN) model to achieve segmentation by combining clue points and biomedical image. We validate the performance of our approach on five public biomedical image datasets. The experimental results show that the proposed approach outperforms several SOTA methods in multiple metrics.