MF-Net: Multiple-feature extraction network for breast lesion segmentation in ultrasound images

Abstract

Objective:Breast lesion segmentation in ultrasound images is of great significance for qualitative breast lesions. However, blurred lesion boundaries, irregular lesion shapes, and similar intensity distributions between lesion and background bring challenges to accurately segmenting breast lesions. Recently, several U-Net-based variants and transformer-based networks have been applied in breast lesion segmentation. Nevertheless, these methods have three limitations: (1) Introducing mass attention mechanisms and complex operations, (2) Ignoring the capability of extracting local detail features, and (3) Ignoring fusing different global semantic information. Approach:To alleviate these challenges, we propose a novel multiple-feature extraction network named MF-Net. The core designs are as follows: (1) A transformer-based auxiliary bi-encoder models long-range dependencies, (2) A multiple-feature extraction module excavates the robust local features, and (3) A global feature enhancement module integrates different global context information. Main results:We conducted extensive experiments on three public breast lesion datasets and compared our method with twelve state-of-the-art methods. The results of comparison experiments indicate that our model achieves a 3.16% improvement in boundary accuracy with a slight lead in segmentation accuracy. Similarly, when the boundary accuracy is close, our model also gets a 3.92% improvement in segmentation accuracy. In the generalization experiments, our network also demonstrates varying degrees of superiority in segmentation accuracy and boundary precision. Significance:Our network mainly has three advantages: (1) It achieves outstanding performance in the precise segmentation of the breast lesions, (2) It focuses on exploring details such as irregular morphology and blurred boundaries of breast lesions, providing valuable insights for further clinical research, and (3) It improves performance without decreasing inference speed through the introduction of critical components.