You Need Glimpse Before Segmentation: Stochastic Detector-Actor-Critic for Medical Image Segmentation

Abstract

Medical images often contain more redundant background areas than natural images, potentially introducing noise and degrading image segmentation performance. Inspired by doctors' diagnostic processes, where they identify the lesion area before conducting a detailed analysis, we introduce a novel Stochastic Detector-Actor-Critic (SDAC) framework to tackle this challenge. SDAC initially glimpses the entire image using a detector network and policy gradient algorithms to filter out irrelevant background regions and focus on crucial, smaller areas for segmentation. The Actor-Critic algorithm then dynamically creates segmentation masks pixel by pixel without user intervention or coarse masks, forming a robust segmentation module. Both processes are trained jointly to reduce error propagation and ensure stability and ease of implementation. Our experiments on two commonly used medical image segmentation datasets demonstrate that SDAC achieves competitive results comparable to state-of-the-art methods while using 10x fewer parameters than the best-performing baseline in terms of DICE and IoU metrics. We also conduct detailed ablation studies to enhance understanding and facilitate practical use. Furthermore, SDAC performs well in low-resource settings (i.e., 50-shot or 100-shot), making it ideal for real-world scenarios. Its lightweight design make SDAC an excellent baseline for medical image segmentation tasks.