Breast Segmentation of MRI Based on U-Net and Multi-Headed Self-Attention Mechanism

In breast MRI, overall breast segmentation is a key step in performing breast cancer risk assessment. To achieve automatic and accurate breast segmentation in breast MR images, we propose a breast segmentation model based on U-Net and multi-head self-attention mechanism, which adds global information through multi-head self-attention module and changes the cascade structure in U-Net network to pixel-by-pixel summation. On the breast MRI dataset, the proposed model can achieve accurate, effective and fast breast segmentation with an average DSC and an average MIOU of 97.28 % and 92.01 %, respectively, which are 4.7 % and 5.56 % higher compared to U-Net, respectively.