Fully Automated Deep Learning Method for Fibroglandular Tissue Segmentation in Breast MRI

Studies have demonstrated that the relative amount of fibroglandular tissue (FGT) with respect to fatty tissue on breast MRI is a risk factor for breast cancer. The amount of FGT is typically assessed qualitatively by a radiologist or by semi-quantitative tools, but both methods suffer from inter-reader variability and are imprecise. The additional challenge is a lack of publicly available data for development and comparison of quantitative tools. The objective of this study was: (i) to develop fully automated segmentation methods for breast and FGT based on convolutional neural networks (CNN) and (ii) to publicly share annotation masks and radiologists scores for the utilized images. 127 fat-saturated gradient echo T1-weighted pre-contrast MRI studies were used for model development and evaluation. The models were evaluated using the Dice similarity coefficient (DSC). Additionally, we implemented and evaluated an algorithm for breast density assessment which utilized our segmentation models. In the test dataset, the DSC was 0.879 for the breast and 0.730 for the FGT. The correlation between median radiologist breast density assessment and FGT percentage assessed by our model was r=0.83 (p<0.01). Overall, the model demonstrated high accuracy compared to gold standard radiologist assessment. The data and the model were made publicly available.