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

Haoyu Dong, Hanxue Gu, Yaqian Chen

IEEE Transactions on Bio-Medical Engineering

|January 12, 2026

Summary

This summary is machine-generated.

Segment Anything Model 2 (SAM 2) shows promise for 3D medical image segmentation. Optimized prompting strategies significantly improve its performance, approaching clinical usefulness for segmenting complex medical scans.

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

The Segment Anything Model (SAM) excels at prompt-based image segmentation.
SAM 2 extends this capability to video segmentation, offering potential for 3D medical image analysis.

Purpose of the Study:

To comprehensively evaluate SAM 2's performance in segmenting 2D and 3D medical images.
To identify optimal prompting strategies for SAM 2 in the context of 3D medical imaging.

Main Methods:

Extensive evaluation of SAM 2 across 21 medical imaging datasets (2D and 3D modalities, surgical videos).
Testing 80 different prompt strategies, including point, box, and mask prompts, across various propagation methods.
Comparative analysis of SAM 2's performance with different prompting techniques and propagation methods.

Main Results:

SAM 2 performs comparably to SAM in 2D medical image segmentation.
In 3D settings, specific strategies like selecting the first mask, prompting the largest object slice, and using box prompts yield better results.
Without fine-tuning, SAM 2 achieves 3D IoU scores from 0.32 to over 0.8, demonstrating increasing clinical utility with optimized prompts.

Conclusions:

SAM 2 demonstrates significant potential for 3D medical image segmentation.
Proposed prompting strategies enhance SAM 2's effectiveness beyond default settings.
Findings provide practical guidance for leveraging SAM 2 in prompt-based 3D medical image segmentation.