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Abstract #0284

Independent Validation of U-Net Based Breast and Fibroglandular Tissue Segmentation Method on MRI Datasets Acquired Using Different Scanners

Yang Zhang1, Jeon-Hor Chen1,2, Kai-Ting Chang1, Vivian Youngjean Park3, Min Jung Kim3, Siwa Chan4, Peter Chang1, Daniel Chow1, Alex Luk1, Tiffany Kwong1, 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 Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Democratic People's Republic of, 4Department of Medical Imaging, Taichung Tzu-Chi Hospital, Taichung, Taiwan

Segmentation of breast and fibroglandular tissue (FGT) using the U-net architecture was implemented using training MRI from 286 patients, and the developed model was tested in independent validation datasets from 28 healthy women acquired using 4 different MR scanners. The dice similarity coefficient was 0.86 for breast, 0.83 for FGT; and the accuracy was 0.94 for breast and 0.93 for FGT. The results on MRI acquired using different MR scanners were similar. U-net provides a fully automatic, efficient, segmentation method in large MRI datasets for evaluating its role on breast cancer risk assessment and hormonal therapy response prediction.

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