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Abstract #4740

Development of U-Net Breast Density Segmentation Method for Fat-Sat T1-Weighted Images Using Transfer Learning from Model for Non-Fat-Sat Images

Yang Zhang1, Jeon-Hor Chen1,2, Kai-Ting Chang1, Siwa Chan3, Huay-Ben Pan4, Jiejie Zhou5, Ouchen Wang6, Meihao Wang5, and Min-Ying Lydia Su1

1Department of Radiological Sciences, University of California, Irvine, CA, United States, 2Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan, 3Department of Medical Imaging, Taichung Tzu-Chi Hospital, Taichung, Taiwan, 4Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, 5Department of Radiology, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China, 6Department of Thyroid and Breast Surgery, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China

The U-Net deep learning is a feasible method for segmentation of breast and fibroglandular tissue on non-fat-suppressed (non-fat-sat) T1-weighted images. Whether it can work on fat-sat images, which are more commonly used for diagnosis, is studied. Three datasets were used: 126 Training, 62 Testing Set-A, and 41 Testing Set-B. The model was developed without and with transfer learning based on parameters in the previous model developed for non-fat-sat images. The results show that U-Net can also achieve a high segmentation accuracy for fat-sat images, and when training case number is small, transfer learning can help to improve accuracy.

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