A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. (October 2020) - Pérez-Benito, Francisco Javier; Signol, François; Perez-Cortes, Juan-Carlos; Fuster-Baggetto, Alejandro; Pollan, Marina; Pérez-Gómez, Beatriz; Salas-Trejo, Dolores; Casals, Maria; Martínez, Inmaculada; LLobet, Rafael
Highlights: An entirely convolutional-based deep learning architecture for parametric dense tissue segmentation in Full Field Digital Mammographies. A preprocessing step, dealing with distribution differences among mammograms from different acquisition devices, improves the system performance. An ad hoc, continuous, and differentiable loss function was developed to best fit segmentations made by radiologists. The concordance segmentation scores suggest this system could be used as an independent reader. Abstract: Background and Objective: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. Methods: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.