ESAM2-BLS: Enhanced segment anything model 2 for efficient breast lesion segmentation in ultrasound imaging

Ultrasound imaging, as an economical, efficient, and non-invasive diagnostic tool, is widely used for breast lesion screening and diagnosis. However, the segmentation of lesion regions remains a significant challenge due to factors such as noise interference and the variability in image quality. To address this issue, we propose a novel deep learning model named enhanced segment anything model 2 (SAM2) for breast lesion segmentation (ESAM2-BLS). This model is an optimized version of the SAM2 architecture. ESAM2-BLS customizes and fine-tunes the pre-trained SAM2 model by introducing an adapter module, specifically designed to accommodate the unique characteristics of breast ultrasound images. The adapter module directly addresses ultrasound-specific challenges including speckle noise, low contrast boundaries, shadowing artifacts, and anisotropic resolution through targeted architectural elements such as channel attention mechanisms, specialized convolution kernels, and optimized skip connections. This optimization significantly improves segmentation accuracy, particularly for low-contrast and small lesion regions. Compared to traditional methods, ESAM2-BLS fully leverages the generalization capabilities of large models while incorporating multi-scale feature fusion and axial dilated depthwise convolution to effectively capture multi-level information from complex lesions. During the decoding process, the model enhances the identification of fine boundaries and small lesions through depthwise separable convolutions and skip connections, while maintaining a low computational cost. Visualization of the segmentation results and interpretability analysis demonstrate that ESAM2-BLS achieves an average Dice score of 0.9077 and 0.8633 in five-fold cross-validation across two datasets with over 1600 patients. These results significantly improve segmentation accuracy and robustness. This model provides an efficient, reliable, and specialized automated solution for early breast cancer screening and diagnosis.