School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, People's Republic of China. Guangdong Province Key Laboratory Computational Science, Sun Yat-Sen University, Guangzhou, People's Republic of China.
Phys Med Biol. 2020 May 19;65(10):105006. doi: 10.1088/1361-6560/ab7e7f.
Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in magnetic resonance imaging (MRI), which is useful for breast cancer risk assessment. In this study, we develop an automated deep learning method based on a generative adversarial network (GAN) to identify the FGT region in MRI volumes and evaluate its impact on a specific clinical application. The GAN consists of an improved U-Net as a generator to generate FGT candidate areas and a patch deep convolutional neural network (DCNN) as a discriminator to evaluate the authenticity of the synthetic FGT region. The proposed method has two improvements compared to the classical U-Net: (1) the improved U-Net is designed to extract more features of the FGT region for a more accurate description of the FGT region; (2) a patch DCNN is designed for discriminating the authenticity of the FGT region generated by the improved U-Net, which makes the segmentation result more stable and accurate. A dataset of 100 three-dimensional (3D) bilateral breast MRI scans from 100 patients (aged 22-78 years) was used in this study with Institutional Review Board (IRB) approval. 3D hand-segmented FGT areas for all breasts were provided as a reference standard. Five-fold cross-validation was used in training and testing of the models. The Dice similarity coefficient (DSC) and Jaccard index (JI) values were evaluated to measure the segmentation accuracy. The previous method using classical U-Net was used as a baseline in this study. In the five partitions of the cross-validation set, the GAN achieved DSC and JI values of 87.0 ± 7.0% and 77.6 ± 10.1%, respectively, while the corresponding values obtained through by the baseline method were 81.1 ± 8.7% and 69.0 ± 11.3%, respectively. The proposed method is significantly superior to the previous method using U-Net. The FGT segmentation impacted the BPE quantification application in the following manner: the correlation coefficients between the quantified BPE value and BI-RADS BPE categories provided by the radiologist were 0.46 ± 0.15 (best: 0.63) based on GAN segmented FGT areas, while the corresponding correlation coefficients were 0.41 ± 0.16 (best: 0.60) based on baseline U-Net segmented FGT areas. BPE can be quantified better using the FGT areas segmented by the proposed GAN model than using the FGT areas segmented by the baseline U-Net.
纤维腺体组织（FGT）分割是磁共振成像（MRI）中背景实质强化（BPE）定量分析的关键步骤，这对于乳腺癌风险评估很有用。在这项研究中，我们开发了一种基于生成对抗网络（GAN）的自动化深度学习方法，用于识别 MRI 容积中的 FGT 区域，并评估其对特定临床应用的影响。GAN 由改进的 U-Net 作为生成器组成，用于生成 FGT 候选区域，由补丁深度卷积神经网络（DCNN）作为鉴别器，用于评估合成 FGT 区域的真实性。与经典的 U-Net 相比，该方法有两个改进：（1）改进的 U-Net 旨在提取 FGT 区域的更多特征，以更准确地描述 FGT 区域；（2）设计了一个补丁 DCNN 来区分改进的 U-Net 生成的 FGT 区域的真实性，这使得分割结果更加稳定和准确。本研究使用了 100 名患者（年龄 22-78 岁）的 100 个三维（3D）双侧乳腺 MRI 扫描数据集，经机构审查委员会（IRB）批准。所有乳房的 3D 手动分割 FGT 区域被用作参考标准。模型的训练和测试采用五折交叉验证。使用 Dice 相似系数（DSC）和 Jaccard 指数（JI）值来衡量分割精度。本研究将使用经典 U-Net 的先前方法作为基线。在交叉验证集的五个分区中，GAN 分别达到了 87.0±7.0%和 77.6±10.1%的 DSC 和 JI 值，而基线方法的相应值分别为 81.1±8.7%和 69.0±11.3%。与使用 U-Net 的先前方法相比，该方法具有显著优势。FGT 分割以以下方式影响 BPE 定量应用：基于 GAN 分割的 FGT 区域，定量 BPE 值与放射科医师提供的 BI-RADS BPE 类别之间的相关系数为 0.46±0.15（最佳值为 0.63），而基于基线 U-Net 分割的 FGT 区域的相应相关系数为 0.41±0.16（最佳值为 0.60）。与使用基线 U-Net 分割的 FGT 区域相比，使用所提出的 GAN 模型分割的 FGT 区域可以更好地量化 BPE。
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