  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.
