Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
The reliance on large labeled datasets presents a significant challenge in
medical image segmentation. Few-shot learning offers a potential solution, but
existing methods often still require substantial training data. This paper
proposes a novel approach that leverages the Segment Anything Model 2 (SAM2), a
vision foundation model with strong video segmentation capabilities. We
conceptualize 3D medical image volumes as video sequences, departing from the
traditional slice-by-slice paradigm. Our core innovation is a support-query
matching strategy: we perform extensive data augmentation on a single labeled
support image and, for each frame in the query volume, algorithmically select
the most analogous augmented support image. This selected image, along with its
corresponding mask, is used as a mask prompt, driving SAM2's video
segmentation. This approach entirely avoids model retraining or parameter
updates. We demonstrate state-of-the-art performance on benchmark few-shot
medical image segmentation datasets, achieving significant improvements in
accuracy and annotation efficiency. This plug-and-play method offers a powerful
and generalizable solution for 3D medical image segmentation.
