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HATSAM: hierarchical adaptation strategy for segment anything model in medical imaging
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DOI：10.1007/s11760-026-05288-4.png)
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Abstract
En 中文
While the Segment Anything Model (SAM) excels in natural image segmentation, its application to medical imaging, especially ultrasound, faces challenges like artifacts and low-contrast boundaries, hindering domain adaptation. Existing methods often underutilize SAM's pre-trained potential, inadequately optimizing both parameter and feature spaces, leading to suboptimal trade-offs between efficiency and accuracy. This paper introduces HATSAM (Hierarchical Adaptation Strategy for Segment Anything Model), a hierarchical adaptation network for efficient SAM transfer in medical image segmentation. HATSAM addresses parameter space adaptability in the image encoder with a Parameter Space Completeness Dynamic Adaptation (PSCDA) module, which combines adapter-based fine-tuning for incomplete parameters with SVD-based singular value updates for relatively complete ones. For feature space misalignment in the mask decoder, the Detail Enhanced Feature Fusion Module (DEFFM) denoises fine-grained features and integrates them with global ones. HATSAM effectively adapts SAM for ultrasound segmentation with lower computational cost. Extensive experiments on seven public datasets (TN3K, TG3K, BUSI, CAMUS, DDTI, UDIAT, EchoCP) show HATSAM achieves a 21.32% inference speed improvement and a 36.97% computational cost reduction over state-of-the-art approaches, while maintaining competitive accuracy.
Keywords:
SAM
Medical image segmentation
Domain adaptation
Ultrasound image Processing
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