An accurate fibroglandular tissue (FGT) segmentation model was designed using of a deep learning strategy on T1w series without fat suppression. The proposed method combined a dedicated preprocessing and the training of a two-dimensional U-Net architecture on a multi-centric representative database to achieve an automatic FGT segmentation. The final test of the generated model exhibited overall good performances with a median Dice similarity coefficient of 0.951. More contrasted performances were obtained when correlating the gland density with the discrepancy between ground truth and prediction. Indeed, the lower the breast density, the greater the uncertainty in the segmentation.
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