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
Instituto Tecnológico de la Informática, Universitat Politècnica de València, Camino de Vera, s/n, València 46022, Spain.
National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos 5, Madrid 28029, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos 5, Madrid 28029, Spain.
Comput Methods Programs Biomed. 2020 Oct;195:105668. doi: 10.1016/j.cmpb.2020.105668. Epub 2020 Jul 24.
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.
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.
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.
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.
乳腺癌是女性中最常见的癌症。西班牙医疗保健网络在所有自治区建立了基于人群的筛查项目，对无症状女性进行乳房X光检查以实现早期诊断。从数字化乳房X光片中评估的乳房密度是一种已知与患乳腺癌风险较高相关的生物标志物。因此，提供一种从乳房X光片中测量乳房密度的可靠方法至关重要。此外，随着乳房X光片数量每天都在增加，这种分割过程的完全自动化正变得至关重要。重要的挑战与不同设备图像的差异以及缺乏客观的金标准有关。本文提出了一个基于深度学习的全自动框架来估计乳房密度。该框架涵盖乳房检测、胸肌排除和纤维腺组织分割。
一项多中心研究，由1785名女性组成，其“用于展示”的乳房X光片由两名经验丰富的放射科医生进行分割。6680张乳房X光片中的4992张被用作训练语料库，其余的（1688张）形成测试语料库。本文提出了一个直方图归一化步骤，该步骤平滑了采集之间的差异，一种将分割参数作为内在图像特征进行学习的回归架构，以及一个基于DICE分数的损失函数。
获得的结果表明，当将自动框架与放射科医生最接近的乳房分割结果进行比较时，自动框架达到了两名放射科医生所达到的一致性水平（DICE分数）（0.77）。对于使用质量最高的设备采集的图像，每个采集设备的DICE分数达到0.84，而放射科医生之间的一致性为0.76。
与两名经验丰富的放射科医生相比，基于深度学习的自动乳房密度估计器表现出相似的性能。这表明该系统可用于支持放射科医生减轻其工作负担。
