SAM-aware Test-time Adaptation for Universal Medical Image Segmentation
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909981458038784 |
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| author | Wu, Jianghao Wu, Yicheng Xie, Yutong Bai, Wenjia Zhang, You Tang, Feilong Li, Yulong Razzak, Imran Schmidt, Daniel F George, Yasmeen |
| author_facet | Wu, Jianghao Wu, Yicheng Xie, Yutong Bai, Wenjia Zhang, You Tang, Feilong Li, Yulong Razzak, Imran Schmidt, Daniel F George, Yasmeen |
| contents | Leveraging the Segment Anything Model (SAM) for medical image segmentation remains challenging due to its limited adaptability across diverse medical domains. Although fine-tuned variants, such as MedSAM, improve performance in scenarios similar to the training modalities or organs, they may lack generalizability to unseen data. To overcome this limitation, we propose SAM-aware Test-time Adaptation (SAM-TTA), a lightweight and flexible framework that preserves SAM's inherent generalization ability while enhancing segmentation accuracy for medical images. SAM-TTA tackles two major challenges: (1) input-level discrepancy caused by channel mismatches between natural and medical images, and (2) semantic-level discrepancy due to different object characteristics in natural versus medical images (e.g., with clear boundaries vs. ambiguous structures). To this end, we introduce two complementary components: a self-adaptive Bezier Curve-based Transformation (SBCT), which maps single-channel medical images into SAM-compatible three-channel images via a few learnable parameters to be optimized at test time; and IoU-guided Multi-scale Adaptation (IMA), which leverages SAM's intrinsic IoU scores to enforce high output confidence, dual-scale prediction consistency, and intermediate feature consistency, to improve semantic-level alignments. Extensive experiments on eight public medical image segmentation tasks, covering six grayscale and two color (endoscopic) tasks, demonstrate that SAM-TTA consistently outperforms state-of-the-art test-time adaptation methods. Notably, on six grayscale datasets, SAM-TTA even surpasses fully fine-tuned models, achieving significant Dice improvements (i.e., average 4.8% and 7.4% gains over MedSAM and SAM-Med2D) and establishing a new paradigm for universal medical image segmentation. Code is available at https://github.com/JianghaoWu/SAM-TTA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05221 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAM-aware Test-time Adaptation for Universal Medical Image Segmentation Wu, Jianghao Wu, Yicheng Xie, Yutong Bai, Wenjia Zhang, You Tang, Feilong Li, Yulong Razzak, Imran Schmidt, Daniel F George, Yasmeen Computer Vision and Pattern Recognition Leveraging the Segment Anything Model (SAM) for medical image segmentation remains challenging due to its limited adaptability across diverse medical domains. Although fine-tuned variants, such as MedSAM, improve performance in scenarios similar to the training modalities or organs, they may lack generalizability to unseen data. To overcome this limitation, we propose SAM-aware Test-time Adaptation (SAM-TTA), a lightweight and flexible framework that preserves SAM's inherent generalization ability while enhancing segmentation accuracy for medical images. SAM-TTA tackles two major challenges: (1) input-level discrepancy caused by channel mismatches between natural and medical images, and (2) semantic-level discrepancy due to different object characteristics in natural versus medical images (e.g., with clear boundaries vs. ambiguous structures). To this end, we introduce two complementary components: a self-adaptive Bezier Curve-based Transformation (SBCT), which maps single-channel medical images into SAM-compatible three-channel images via a few learnable parameters to be optimized at test time; and IoU-guided Multi-scale Adaptation (IMA), which leverages SAM's intrinsic IoU scores to enforce high output confidence, dual-scale prediction consistency, and intermediate feature consistency, to improve semantic-level alignments. Extensive experiments on eight public medical image segmentation tasks, covering six grayscale and two color (endoscopic) tasks, demonstrate that SAM-TTA consistently outperforms state-of-the-art test-time adaptation methods. Notably, on six grayscale datasets, SAM-TTA even surpasses fully fine-tuned models, achieving significant Dice improvements (i.e., average 4.8% and 7.4% gains over MedSAM and SAM-Med2D) and establishing a new paradigm for universal medical image segmentation. Code is available at https://github.com/JianghaoWu/SAM-TTA. |
| title | SAM-aware Test-time Adaptation for Universal Medical Image Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05221 |