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Abstract #1549

Automated Deep Learning Method for Whole-Breast Segmentation in Synthetic MRI

Weibo Gao1, Yuelang Zhang1, Yuwei Xia2, and Yuhui Xiong3
1The Second Affiliated Hospital of Xi 'an Jiaotong University, Xi'an, China, 2Department of Research and Development, United Imaging Intelligence, Xi'an, China, 3GE HealthCare MR Research, Beijing, China

Synopsis

Keywords: AI Diffusion Models, Breast

Motivation: In quantitative breast MRI studies, segmenting the whole-breast region is a key initial step for several clinically relevant applications.

Goal(s): To develop a deep learning segmentation method utilizing the nnU-Net architecture for fully automated whole-breast segmentation based on diffusion-weighted imaging (DWI) and synthetic MRI (SyMRI) images.

Approach: The U-Net and nnU-Net deep learning algorithms were employed to segment the whole-breast on DWI and SyMRI images.

Results: The nnU-Net outperformed the U-Net and the PD of SyMRI exhibited better performance than DWI.

Impact: The nnU-Net exhibited exceptional segmentation performance for fully automated breast segmentation of contrast-free quantitative images.

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Keywords