Breast cancer is one of the most common malignant tumors among women worldwide. Accurate lesion segmentation plays a crucial role in the early diagnosis and treatment of breast cancer. However, due to the variability of lesion morphology and the complexity of ultrasound imaging mechanisms, existing deep learning-based methods for breast ultrasound lesion segmentation still face significant challenges in segmentation accuracy.To further improve the segmentation precision of lesion regions in breast ultrasound images, this paper proposes a novel lesion segmentation network, CWSASK-BBA-Net, based on the classic U-Net architecture. First, a Channel-wise Spatially Adaptive Selective Kernel Convolution Module (CWSASKM) is introduced, which adaptively selects the receptive field size at each spatial location based on the semantic features of different channels, thereby enhancing the network’s capability to model multi-scale information. Second, a Bidirectional Boundary-Aware Mechanism (BBAM) is integrated to jointly model salient targets and their boundaries through forward and reverse attention, progressively improving the discrimination of non-salient regions and further strengthening the representation of boundary information.To validate the performance of CWSASK-BBA-Net, segmentation experiments were conducted on three publicly available breast ultrasound image datasets. Results show that the proposed method achieves average values of 71.97%, 82.85%, 81.40%, and 80.44% on the BUSI dataset in terms of Jaccard index, precision, recall, and Dice similarity coefficient, respectively, with improvements of 1.69%, 1.05%, 1.28%, and 1.84% compared to the second-best methods. On the UDIAT dataset, the four metrics reach 78.14%, 88.31%, 86.73%, and 86.10%, with gains of 2.75%, 2.04%, 0.56%, and 2.01%, respectively. In addition, the method also demonstrates overall performance advantages on the external STU dataset. These results indicate that CWSASK-BBA-Net exhibits superior overall performance in breast ultrasound image segmentation tasks.