Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation

• URL: http://arxiv.org/abs/2501.09138v1
• Date: Wed, 15 Jan 2025 20:44:21 GMT
• Title: Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation
• Authors: Xingxin He, Yifan Hu, Zhaoye Zhou, Mohamed Jarraya, Fang Liu,
• Abstract summary: Few-shot Adaptation of Training-frEe SAM (FATE-SAM) is a novel method designed to adapt the advanced Segment Anything Model 2 (SAM2) for 3D medical image segmentation.<n>FATE-SAM reassembles pre-trained modules of SAM2 to enable few-shot adaptation, leveraging a small number of support examples.<n>We evaluate FATE-SAM on multiple medical imaging datasets and compare it with supervised learning methods, zero-shot SAM approaches, and fine-tuned medical SAM methods.
• Score: 8.78725593323412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision foundation models have achieved remarkable progress across various image analysis tasks. In the image segmentation task, foundation models like the Segment Anything Model (SAM) enable generalizable zero-shot segmentation through user-provided prompts. However, SAM primarily trained on natural images, lacks the domain-specific expertise of medical imaging. This limitation poses challenges when applying SAM to medical image segmentation, including the need for extensive fine-tuning on specialized medical datasets and a dependency on manual prompts, which are both labor-intensive and require intervention from medical experts. This work introduces the Few-shot Adaptation of Training-frEe SAM (FATE-SAM), a novel method designed to adapt the advanced Segment Anything Model 2 (SAM2) for 3D medical image segmentation. FATE-SAM reassembles pre-trained modules of SAM2 to enable few-shot adaptation, leveraging a small number of support examples to capture anatomical knowledge and perform prompt-free segmentation, without requiring model fine-tuning. To handle the volumetric nature of medical images, we incorporate a Volumetric Consistency mechanism that enhances spatial coherence across 3D slices. We evaluate FATE-SAM on multiple medical imaging datasets and compare it with supervised learning methods, zero-shot SAM approaches, and fine-tuned medical SAM methods. Results show that FATE-SAM delivers robust and accurate segmentation while eliminating the need for large annotated datasets and expert intervention. FATE-SAM provides a practical, efficient solution for medical image segmentation, making it more accessible for clinical applications.

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