Training-Free Breast Ultrasound Image Segmentation with Retrieval-based SAM2

Objective: Early detection and treatment are crucial for improving the prognosis of breast cancer. While ultrasound imaging is widely utilized for screening, its interpretation is subjective and heavily dependent on clinician expertise. Although supervised models have shown great promise in medical image segmentation, they encounter challenges such as the need for large labeled datasets, extended training times, and limited generalization to external datasets. Methods: We propose TFSeg, a Training-Free automatic image segmentation framework based on Segment Anything Model 2 (SAM2). TFSeg eliminates the need for re-training new models and tuning hyper-parameters by generating image sequences through the retrieval of the top-k most similar images from the training set, using their mask prompts to guide SAM2 in achieving accurate and efficient segmentation. Results: Our approach, validated on a breast ultrasound image dataset, achieved a Dice score of 82.66%, surpassing the performance of most supervised and training-free segmentation models. Notably, it achieved the highest precision (87.93%) among all the compared methods. Conclusion: By combining image retrieval with sequence generation, TFSeg leverages SAM2 to provide an efficient and effective solution for breast cancer ultrasound image segmentation and demonstrates potential for broader applications in medical image analysis. Significance: The Training-Free automatic image Segmentation framework addresses challenges associated with supervised models, making it a promising method for improving breast cancer diagnosis.