Abstract
Medical image segmentation is fundamental for disease diagnosis, particularly in the context of breast cancer, a prevalent malignancy affecting women. The accuracy of lesion localization and preservation of image details are essential for ensuring the integrity of lesion segmentation. However, the low resolution of breast tumor B-mode ultrasound images poses challenges in precisely identifying lesion sites. To address this issue, this study introduces the Mamba architecture model, which combines three foundational models with the long-sequence processing model Mamba to develop a novel segmentation model for breast tumor ultrasound images. The selective mechanism and hardware-aware algorithm of the Mamba model enable longer sequence inputs and faster computing speeds. Moreover, integrating a complete chain of VMamba blocks into the basic model enhances segmentation accuracy and image detail processing capabilities. Experimental segmentation was performed on two benchmark ultrasound datasets (BUSI and BUS-BRA) using both the baseline and improved models. The results were compared using metrics such as Dice and IoU, with additional evaluations conducted under small-sample training conditions. This study is intended to provide guidance for the future development of medical image segmentation. Moreover, the experimental results demonstrate that the model incorporating the Mamba architecture achieves superior performance on breast ultrasound images.