Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models
This provides a practical solution for medical image segmentation with limited annotated data, though it appears incremental as it combines existing foundation models with retrieval mechanisms.
The paper tackles the problem of few-shot medical image segmentation by proposing a retrieval-augmented method that adapts DINOv2 and SAM 2 foundation models, achieving superior performance and generalizability across three medical image segmentation tasks without retraining or finetuning.
Medical image segmentation is crucial for clinical decision-making, but the scarcity of annotated data presents significant challenges. Few-shot segmentation (FSS) methods show promise but often require training on the target domain and struggle to generalize across different modalities. Similarly, adapting foundation models like the Segment Anything Model (SAM) for medical imaging has limitations, including the need for finetuning and domain-specific adaptation. To address these issues, we propose a novel method that adapts DINOv2 and Segment Anything Model 2 (SAM 2) for retrieval-augmented few-shot medical image segmentation. Our approach uses DINOv2's feature as query to retrieve similar samples from limited annotated data, which are then encoded as memories and stored in memory bank. With the memory attention mechanism of SAM 2, the model leverages these memories as conditions to generate accurate segmentation of the target image. We evaluated our framework on three medical image segmentation tasks, demonstrating superior performance and generalizability across various modalities without the need for any retraining or finetuning. Overall, this method offers a practical and effective solution for few-shot medical image segmentation and holds significant potential as a valuable annotation tool in clinical applications.