DVF-YOLO-Seg: A two-stage breast mass segmentation model with enhanced feature extraction and small lesion detection

Objective Accurate segmentation of breast lesions, especially small ones, remains challenging in digital mammography due to complex anatomical structures and low-contrast boundaries. This study proposes DVF-YOLO-Seg, a two-stage segmentation framework designed to improve feature extraction and enhance small-lesion detection performance in mammographic images. Methods The proposed method integrates an enhanced YOLOv10-based detection module with a segmentation stage based on the Visual Reference Prompt Segment Anything Model (VRP-SAM). A novel DualConv module is introduced to improve spatial, visual, and channel feature representation, while Varifocal Loss addresses class imbalance by emphasizing hard-to-detect lesions. The detection results are used to generate bounding box prompts for VRP-SAM, which refines the final lesion segmentation. Results Experiments on the curated breast imaging subset of the digital database for screening mammography dataset demonstrate that DVF-YOLO-Seg achieves a precision of 79.7%, a recall of 81.5%, a dice coefficient of 80.2%, and an F1-score of 80.6%, outperforming baseline models. Particularly for lesions <5 mm, the model shows improved sensitivity. Ablation studies confirm the effectiveness of the DualConv module and Varifocal Loss. Additionally, the framework shows better visual consistency and clearer boundaries in clinician-evaluated results. Conclusion DVF-YOLO-Seg significantly enhances the detection and segmentation accuracy for small breast lesions in mammography. By combining improved detection with prompt-based segmentation, this method offers a promising approach for computer-aided diagnosis in breast cancer screening.