School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
Phys Med Biol. 2018 Jan 9;63(2):025005. doi: 10.1088/1361-6560/aa9f87.
Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm × 800 µm from 100 µm × 100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79 ± 0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC = 0.72 ± 0.18 and r = 0.85. For the independent test set, DCNN achieved DC = 0.76 ± 0.09 and r = 0.94, while feature-based learning achieved DC = 0.62 ± 0.21 and r = 0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.
乳腺密度是与癌症风险最相关的最重要因素之一。本研究旨在开发一种用于自动估计数字乳腺 X 线摄影（DM）中百分比密度（PD）的有监督深度学习方法。输入的“处理”DM 首先进行对数变换，通过多分辨率预处理方案增强，并从 100μm×100μm 像素尺寸下采样到 800μm×800μm。通过使用域自适应重采样方法，深度卷积神经网络（DCNN）用于估计乳腺密度（PMD）的概率图。根据 PMD，PD 估计为致密区域与乳房区域的比值。DCNN 方法与基于特征的统计学习方法进行了比较。提取灰度、纹理和形态特征，并使用最小绝对收缩和选择算子将特征组合为基于特征的 PMD。在机构审查委员会批准下，我们从机构的患者文件中回顾性地收集了一个 478 例 DM 的训练集和一个 183 例 DM 的独立测试集。两名经验丰富的乳腺 X 线摄影质量标准法案放射科医生进行了 PD 的交互式分割，作为参考标准。使用训练集进行了十折交叉验证来进行模型选择和评估。在交叉验证中，DCNN 获得了 0.79±0.13 的 Dice 系数（DC）和 0.97 的 Pearson 相关系数（r），而基于特征的学习获得了 DC=0.72±0.18 和 r=0.85。对于独立测试集，DCNN 实现了 DC=0.76±0.09 和 r=0.94，而基于特征的学习实现了 DC=0.62±0.21 和 r=0.75。与基于特征的学习方法相比，我们的 DCNN 方法在 DM 上自动 PD 估计方面表现出更好的性能和更高的稳健性，这表明它可能用于自动密度报告以及基于模型的风险预测。
