RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning

• URL: http://arxiv.org/abs/2409.04298v2
• Date: Mon, 25 Nov 2024 07:58:24 GMT
• Title: RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning
• Authors: Yunhao Bai, Boxiang Yun, Zeli Chen, Qinji Yu, Yingda Xia, Yan Wang,
• Abstract summary: We introduce RevSAM2, a simple yet effective self-correction framework for medical image segmentation. RevSAM2 achieves superior performance in unseen 3D medical image segmentation tasks without the need for fine-tuning. We are the first to explore the potential of SAM2 in label-efficient medical image segmentation without fine-tuning.
• Score: 4.590933790796203
• License:
• Abstract: The Segment Anything Model 2 (SAM2) has recently demonstrated exceptional performance in zero-shot prompt segmentation for natural images and videos. However, when the propagation mechanism of SAM2 is applied to medical images, it often results in spatial inconsistencies, leading to significantly different segmentation outcomes for very similar images. In this paper, we introduce RevSAM2, a simple yet effective self-correction framework that enables SAM2 to achieve superior performance in unseen 3D medical image segmentation tasks without the need for fine-tuning. Specifically, to segment a 3D query volume using a limited number of support image-label pairs that define a new segmentation task, we propose reverse propagation strategy as a query information selection mechanism. Instead of simply maintaining a first-in-first-out (FIFO) queue of memories to predict query slices sequentially, reverse propagation selects high-quality query information by leveraging support images to evaluate the quality of each predicted query slice mask. The selected high-quality masks are then used as prompts to propagate across the entire query volume, thereby enhancing generalization to unseen tasks. Notably, we are the first to explore the potential of SAM2 in label-efficient medical image segmentation without fine-tuning. Compared to fine-tuning on large labeled datasets, the label-efficient scenario provides a cost-effective alternative for medical segmentation tasks, particularly for rare diseases or when dealing with unseen classes. Experiments on four public datasets demonstrate the superiority of RevSAM2 in scenarios with limited labels, surpassing state-of-the-arts by 12.18% in Dice. The code will be released.

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