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Abstract #3765

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

Hang Yu1, Yuru Guo1, Lizhi Xie2, Zhiheng Liu1, Zichuan Xie3, Chenyang Li1, and Suiping Zhou1
1School of Aerospace Science and Technology,Xidian university, xi'an, China, 2GE Healthcare, Beijing, China, 3Guangzhou institute of technology,Xidian University, Guangzhou, China

Synopsis

Keywords: Segmentation, BreastIn 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.

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Keywords