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In light of the global increase in breast cancer cases and the crucial importance of the density of fibroglandular tissue (FGT) in assessing risk and predicting the course of the disease, the accurate measurement of FGT emerges as a significant challenge in diagnostic imaging. The current study focuses on the automatic segmentation of breast glandular tissue in MRI scans using a deep learning model. The aim is to establish a solid foundation for the development of methods for the precise quantification of fibroglandular tissue. For this purpose, the publicly available ‘Duke Breast Cancer MRI’ dataset was systematically processed to train a deep neural network model utilizing the nnU-Net (‘no-new-Net’) framework, which was then subjected to a quantitative evaluation. The results show the following macro-averaged metrics with standard deviation: Dice Similarity Coefficient 0.827 ± 0.152, accuracy 0.997 ± 0.003, sensitivity 0.825 ± 0.158, and specificity 0.999 ± 0.001. The effectiveness of our model in segmenting FGT is underscored by the high values of the Dice coefficient, Accuracy, Sensitivity, and Specificity, which reflect the precision and reliability of our results. The findings of this study lay a solid foundation for developing automated methods to quantify FGT. Our research efforts, especially driven by clinical studies at the University Hospital Augsburg, are focused on further exploring and validating these potentials.
Pehlivan et al. (Thu,) studied this question.