SurpSAM: Saliency- and Uncertainty-Guided Reinforcement Prompting for Automatic Left Atrial Segmentation

Abstract

Left atrial (LA) CT segmentation is essential for atrial fibrillation assessment, ablation treatment planning, and three-dimensional cardiac structural analysis. However, limited annotations, indistinct anatomical boundaries, and substantial inter-individual variability make accurate segmentation challenging. To address this problem, we propose SurpSAM, a saliency- and uncertainty-guided reinforcement prompt optimization framework for one-shot LA CT segmentation. Given only one annotated reference sample, SurpSAM first constructs an LA-specific structural prior through the la-specific structural prior module (SSPM), which provides anatomy-aware saliency responses on target CT slices. Based on this prior, an uncertainty-aware prompt generation strategy (UPGS) samples complementary positive and negative points to form the initial prompt set for the frozen Segment Anything Model (SAM). Rather than directly using these initial prompts, SurpSAM performs Prompt-Space Action Inference, in which a PPO-trained policy adaptively updates the prompt set according to saliency, uncertainty, and SAM feedback. To improve volumetric structural reliability, a subsequent mask action refinement module constructs multiple candidate masks in the mask space and selects the final segmentation according to saliency consistency, reference prior agreement, topological continuity, and prompt consistency. Experiments on the MM-WHS and ImageCAS-STACOM datasets show that SurpSAM achieves Dice scores of 76.55% and 83.53%, respectively, using only one annotated sample, outperforming existing one-shot segmentation methods and training-free SAM prompting strategies. These results demonstrate the effectiveness of progressive prompt generation, reinforcement-based prompt updating, and mask-space structural refinement for robust one-shot LA CT segmentation.