Image Segmentation of Triple-Negative Breast Cancer by Incorporating Multiscale and Parallel Attention Mechanisms

Abstract

Breast cancer is a highly prevalent cancer. Triple-negative breast cancer (TNBC) is more likely to recur and metastasize than other subtypes of breast cancer. Research on the treatment of TNBC is of great importance, and accurate segmentation of the breast lesion area is an important step in the treatment of TNBC. Currently, the gold standard for tumor segmentation is still sketched manually by doctors, which requires expertise in the field of medical imaging and consumes a great deal of doctors’ time and energy. Automatic segmentation of breast cancer not only reduces the burden of doctors but also improves work efficiency. Therefore, it is of great significance to study the automatic segmentation technique for breast cancer lesion regions. In this paper, a deep-learning-based automatic segmentation algorithm for TNBC images is proposed. The experimental data were dynamic contrast-enhanced magnetic resonance imaging TNBC dataset provided by the Cancer Hospital of Zhengzhou University. The experiments were analyzed by comparing several models with UNet, Attention-UNet, ResUNet, and SegNet and using evaluation indexes such as Dice score and Iou. Compared to UNet, Attention-UNet, ResUNet, and SegNet, the proposed method improved the Dice score by 2.1%, 1.54%, 0.88%, and 9.65%, respectively. The experimental results show that the proposed deep-learning-based TNBC image segmentation model can effectively improve the segmentation performance of TNBC tumors.