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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