A Breast Ultrasound Images Lesion Segmentation Network Based on Channel-Wise Spatially Adaptive Selective Kernel Convolution and Bidirectional Boundary-Aware Mechanism

Breast cancer is the most common malignancy among women worldwide, and accurate lesion segmentation is of great importance for its early diagnosis and treatment. However, due to the high morphological variability of lesions and the inherent complexity of ultrasound imaging, existing deep-learning-based methods still face significant challenges in achieving satisfactory segmentation accuracy for breast ultrasound images. To address this limitation, this study proposed a novel breast-lesion segmentation network, termed CWSASKM-BBAM-Net, which is built upon the classical U-Net architecture. First, a Channel-Wise Spatially Adaptive Selective Kernel Convolution Module (CWSASKM) was introduced to adaptively adjust the receptive field size for each spatial location based on channel-specific semantic features, thereby enhancing the network’s multi-scale representation learning. Second, a Bidirectional Boundary-Aware Mechanism (BBAM) was designed to jointly model salient regions and their boundaries by integrating forward and reverse attention. This mechanism progressively improves the discrimination between non-salient areas and lesion areas, thereby refining boundary delineation. Extensive experiments were conducted on three public breast ultrasound datasets (BUSI, UDIAT, and STU). On BUSI dataset, the proposed method achieved Jaccard index, precision, recall, and Dice similarity coefficient of 71.97%, 82.85%, 81.40%, and 80.44%, respectively, outperforming the second-best approach by margins of 1.69, 1.05, 1.28, and 1.84 percentage points. On UDIAT, it attained 78.14%, 88.31%, 86.73%, and 86.10% on these metrics, with improvements of 2.75, 2.04, 0.56, and 2.01 percentage points, respectively. The proposed method also demonstrated superior overall performance on the external STU dataset. These results collectively demonstrate that CWSASKM-BBAM-Net achieves state-of-the-art performance in breast ultrasound image segmentation tasks.