Informativeness-driven active adaptation of SAM: Structural prompts and contrastive parameter selection for medical...

Accurate segmentation of tubular structures, such as blood vessels and airway trees, is critical across various medical applications. However, this task remains highly challenging due to the diverse global morphologies and thin local structures of these tubular formations. Segment Anything Model (SAM)-based methods have shown impressive performance in image segmentation, yet their reliance on manually crafted prompts makes them unsuitable for fine-grained tubular structures. Moreover, existing methods for fine-tuning SAM have not fully explored how to identify the most informative parameters to maximize adaptation performance. To address these issues, we introduce a novel framework termed Active Adaptation of SAM (A²SAM), which adopts informativeness-driven adaptation as its governing principle for medical tubular segmentation. Specifically, we design a structural prompt generator that automatically produces topology-aware prompts, guiding SAM toward anatomically meaningful tubular structures. The structural uncertainty embedded in these prompts captures variations in global morphology. Furthermore, we find that parameters with strong task-specific influence and a lower risk of disrupting generalizable knowledge are beneficial for adapting SAM. We define these parameters as contrastive parameters and propose a contrastive parameter selection strategy that modulates squared-gradient influence with magnitude-based preservation. The proposed strategy enhances adaptation performance in tubular segmentation and preserves SAM's generalizable knowledge. Finally, we propose a thickness-aware loss function to improve local connectivity in thin structures. The proposed A²SAM is validated on five diverse benchmark datasets for medical tubular segmentation. Comprehensive experiments demonstrate that A²SAM achieves strong overall performance. Our code is publicly available at: https://github.com/YongChen-Exact/A2SAM.