Segment Anything Model 2: An Application to 2D and 3D Medical Images

Abstract

Segment Anything Model (SAM) has gained significant attention because of its ability to segment a variety of objects in images upon providing a prompt. Recently developed SAM 2 has extended this ability to video segmentation, and by substituting the third spatial dimension in 3D images for the time dimension in videos, it opens an opportunity to apply SAM 2 to 3D medical images. In this paper, we extensively evaluate SAM 2's ability to segment both 2D and 3D medical images using 80 prompt strategies across 21 medical imaging datasets, including 2D modalities (X-ray and ultrasound), 3D modalities (magnetic resonance imaging, computed tomography, and positron emission tomography), and surgical videos. We find that in the 2D setting, SAM 2 performs similarly to SAM, while in the 3D setting we observe that: (1) selecting the first mask is more effective than choosing the one with the highest confidence, (2) prompting the slice with the largest object appears is the most cost-effective strategy when only one slice is prompted, (3) box prompts result in higher performance than point prompts at a slightly higher annotation cost, (4) bidirectional propagation outperforms front-to-end propagation, (5) interactive annotation is rarely effective, (6) SAM 2, without fine-tuning, achieves 3D IoU from 0.32 with a single point prompt to 0.51 with a ground truth mask on one slice, and exceeds 0.8 on certain datasets when using box or ground-truth prompts, a level that begins to approach clinical usefulness. These findings demonstrate that SAM 2's ability to segment 3D medical images can be improved with our proposed strategies over the default ones, providing practical guidance for using SAM 2 for prompt-based 3D medical image segmentation.

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