Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationFew-Shot LearningImage SegmentationMedical Image SegmentationSegmentationSemantic SegmentationVideo SegmentationVideo Semantic SegmentationSimilar Papers 제목 키워드 기반
Continual Alignment for SAM: Rethinking Foundation Models for Medical Image Segmentation in Continual Learning
In medical image segmentation, heterogeneous privacy policies across institutions often make joint training on pooled datasets infeasible, motivating continual image segmentation-learning from data streams without catast…
Medical Image SegmentationComputational EfficiencyContinual LearningDenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation
Few-shot medical image semantic segmentation is of paramount importance in the domain of medical image analysis. However, existing methodologies grapple with the challenge of data scarcity during the training phase, lead…
Image SegmentationMedical Image AnalysisMedical Image SegmentationSegmentation+1Rethinking Few-Shot Medical Segmentation: A Vector Quantization View
The existing few-shot medical segmentation networks share the same practice that the more prototypes, the better performance. This phenomenon can be theoretically interpreted in Vector Quantization (VQ) view: the mor…
QuantizationSegmentationSelf-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging
Producing high-quality segmentation masks for medical images is a fundamental challenge in biomedical image analysis. Recent research has explored large-scale supervised training to enable segmentation across various med…
Cell SegmentationDiagnosticImage SegmentationLesion Segmentation+3Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation
Vision foundation models have achieved remarkable progress across various image analysis tasks. In the image segmentation task, foundation models like the Segment Anything Model (SAM) enable generalizable zero-shot segme…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1