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 Chen^1,2, 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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