UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation

• URL: http://arxiv.org/abs/2511.15771v1
• Date: Wed, 19 Nov 2025 17:42:49 GMT
• Title: UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation
• Authors: Yue Li, Qing Xu, Yixuan Zhang, Xiangjian He, Qian Zhang, Yuan Yao, Fiseha B. Tesem, Xin Chen, Ruili Wang, Zhen Chen, Chang Wen Chen,
• Abstract summary: The Segment Anything Model 2 (SAM2) demonstrates remarkable universal segmentation capabilities on natural images.<n>This limitation raises two critical challenges: how to efficiently adapt SAM2 to ultrasound imaging while maintaining parameter efficiency, and how to deploy the adapted model effectively in resource-constrained clinical environments.<n>We propose UniUltra for universal ultrasound segmentation. Specifically, we first introduce a novel context-edge hybrid adapter (CH-Adapter) that enhances fine-grained perception across diverse ultrasound imaging modalities while achieving parameter-efficient fine-tuning.
• Score: 47.18256805790662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Segment Anything Model 2 (SAM2) demonstrates remarkable universal segmentation capabilities on natural images. However, its performance on ultrasound images is significantly degraded due to domain disparities. This limitation raises two critical challenges: how to efficiently adapt SAM2 to ultrasound imaging while maintaining parameter efficiency, and how to deploy the adapted model effectively in resource-constrained clinical environments. To address these issues, we propose UniUltra for universal ultrasound segmentation. Specifically, we first introduce a novel context-edge hybrid adapter (CH-Adapter) that enhances fine-grained perception across diverse ultrasound imaging modalities while achieving parameter-efficient fine-tuning. To further improve clinical applicability, we develop a deep-supervised knowledge distillation (DSKD) technique that transfers knowledge from the large image encoder of the fine-tuned SAM2 to a super lightweight encoder, substantially reducing computational requirements without compromising performance. Extensive experiments demonstrate that UniUltra outperforms state-of-the-arts with superior generalization capabilities. Notably, our framework achieves competitive performance using only 8.91% of SAM2's parameters during fine-tuning, and the final compressed model reduces the parameter count by 94.08% compared to the original SAM2, making it highly suitable for practical clinical deployment. The source code is available at https://github.com/xq141839/UniUltra.

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