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.

中文翻译：

---

使用生成对抗网络在乳房MRI中自动进行纤维腺腺组织分割。

纤维腺腺组织（FGT）分割是对磁共振成像（MRI）中背景实质增强（BPE）进行定量分析的关键步骤，可用于乳腺癌风险评估。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，以识别MRI卷中的FGT区域并评估其对特定临床应用的影响。GAN由改进的U-Net作为生成器来生成FGT候选区域，以及由补丁深度卷积神经网络（DCNN）作为鉴别器来评估合成FGT区域的真实性组成。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，以便更准确地描述FGT区域；（2）设计了补丁DCNN来区分由改进的U-Net生成的FGT区域的真实性，从而使分割结果更加稳定和准确。经机构审查委员会（IRB）批准，本研究使用了100位患者（22-78岁）的100次三维（3D）双侧乳房MRI扫描数据集。提供所有乳房的3D手工分割的FGT区域作为参考标准。五重交叉验证用于模型的训练和测试。评估Dice相似度系数（DSC）和Jaccard指数（JI）值以测量分割精度。本研究使用以前使用经典U-Net的方法作为基准。在交叉验证集的五个分区中，GAN的DSC和JI值分别达到87.0±7.0％和77.6±10.1％，基线法测得的相应值分别为81.1±8.7％和69.0±11.3％。所提出的方法明显优于使用U-Net的先前方法。FGT分割通过以下方式影响BPE定量应用：基于GAN分割的FGT区域，放射线医师提供的量化BPE值与BI-RADS BPE类别之间的相关系数为0.46±0.15（最佳：0.63）。基于基线U-Net分割的FGT面积，相关系数为0.41±0.16（最佳：0.60）。使用建议的GAN模型分割的FGT区域比使用基准U-Net分割的FGT区域可以更好地量化BPE。所提出的方法明显优于使用U-Net的先前方法。FGT分割通过以下方式影响BPE定量应用：基于GAN分割的FGT面积，放射线医师提供的量化BPE值与BI-RADS BPE类别之间的相关系数为0.46±0.15（最佳：0.63）。基于基线U-Net分割的FGT面积，相关系数为0.41±0.16（最佳：0.60）。使用建议的GAN模型分割的FGT区域比使用基准U-Net分割的FGT区域可以更好地量化BPE。所提出的方法明显优于使用U-Net的先前方法。FGT分割通过以下方式影响BPE定量应用：基于GAN分割的FGT区域，放射线医师提供的量化BPE值与BI-RADS BPE类别之间的相关系数为0.46±0.15（最佳：0.63）。基于基线U-Net分割的FGT面积，相关系数为0.41±0.16（最佳：0.60）。使用建议的GAN模型分割的FGT区域比使用基准U-Net分割的FGT区域可以更好地量化BPE。根据GAN分割的FGT面积，放射线医师提供的定量BPE值与BI-RADS BPE类别之间的相关系数为0.46±0.15（最佳：0.63），而基于GAN分割的FGT区域，相应的相关系数为0.41±0.16（最佳：0.60）。基线U-Net分割的FGT区域。使用建议的GAN模型分割的FGT区域比使用基准U-Net分割的FGT区域可以更好地量化BPE。根据GAN分割的FGT面积，放射线医师提供的定量BPE值与BI-RADS BPE类别之间的相关系数为0.46±0.15（最佳：0.63），而基于GAN分割的FGT区域，相应的相关系数为0.41±0.16（最佳：0.60）。基线U-Net分割的FGT区域。使用建议的GAN模型分割的FGT区域比使用基准U-Net分割的FGT区域可以更好地量化BPE。