UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation

TL;DR

UniUltra is a parameter-efficient framework that adapts SAM2 for universal ultrasound segmentation, combining a novel adapter and knowledge distillation to achieve high performance with minimal computational resources.

Contribution

It introduces a hybrid adapter and a knowledge distillation technique to adapt SAM2 for ultrasound segmentation efficiently and effectively.

Findings

Outperforms state-of-the-art methods in ultrasound segmentation

Uses only 8.91% of SAM2's parameters during fine-tuning

Reduces model size by 94.08%, enabling clinical deployment

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.