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 Chen^1,2, 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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