Interactive 3D Medical Image Segmentation with SAM 2
This work addresses the annotation efficiency problem for medical professionals in 3D imaging, but it is incremental as it adapts an existing model to a new domain with limited performance gains.
The paper tackles the problem of interactive 3D medical image segmentation by leveraging SAM 2's zero-shot capabilities to propagate annotations from a single slice to entire volumes, reducing annotation burden; results show it narrows the gap with supervised methods in specific settings, such as on BraTS2020 and medical segmentation decathlon datasets.
Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the generalization and robustness of most IMIS methods. The Segment Anything Model (SAM), though effective for 2D images, requires expensive semi-auto slice-by-slice annotations for 3D medical images. In this paper, we explore the zero-shot capabilities of SAM 2, the next-generation Meta SAM model trained on videos, for 3D medical image segmentation. By treating sequential 2D slices of 3D images as video frames, SAM 2 can fully automatically propagate annotations from a single frame to the entire 3D volume. We propose a practical pipeline for using SAM 2 in 3D medical image segmentation and present key findings highlighting its efficiency and potential for further optimization. Concretely, numerical experiments on the BraTS2020 and the medical segmentation decathlon datasets demonstrate that SAM 2 still has a gap with supervised methods but can narrow the gap in specific settings and organ types, significantly reducing the annotation burden on medical professionals. Our code will be open-sourced and available at https://github.com/Chuyun-Shen/SAM_2_Medical_3D.