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.

中文翻译：

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深度学习系统，用于获取基于阈值的乳房和密集组织分割的最佳参数。

背景与目的

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社区中建立了基于人群的筛查计划，在该计划中，出于早期诊断目的对无症状女性的乳房X光照片进行了检查。通过数字化乳房X线照片评估的乳房密度是已知与罹患乳腺癌的较高风险相关的生物标志物，因此提供一种可靠的方法来从乳房X线照片测量乳房密度至关重要。此外，随着乳房X线照片的数量每天增加，这种分割过程的完全自动化变得至关重要。重要的挑战与来自不同设备的图像差异以及缺乏客观的金标准有关。本文提出了一种基于深度学习的全自动框架，用于估计乳房密度。

方法

一项由1785名妇女组成的多中心研究，由两名经验丰富的放射科医生对乳房X线照片进行“演示”。在6680个乳房X线照片中，总共有4992个用作训练语料，其余（1688）构成了测试语料。本文提出了一种直方图归一化步骤，该步骤可平滑采集之间的差异，一种回归结构，该结构将分割参数作为固有图像特征进行学习，以及一种基于DICE得分的损失函数。

结果

所获得的结果表明，当将自动放射线框架与放射线医师最接近的乳房分割进行比较时，自动放射线框架也可以达到两位放射线医师所达到的一致性（DICE评分）（0.77）。对于质量最高的设备，每个设备的DICE得分达到0.84，而放射科医生之间的一致性为0.76。

结论

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算器具有相似的性能。这表明该系统可用于支持放射科医生简化其工作。