Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study
Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study
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DOI：
10.1148/radiol.2019190737
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发表时间：
2020-01-01
期刊：
影响因子：
19.7
通讯作者：
Nezafat, Reza
中科院分区：
文献类型：
--
作者：
Fahmy, Ahmed S.;Neisius, Ulf;Nezafat, Reza
背景：心脏MRI晚期钆增强（LGE）疤痕体积是肥厚性心肌病（HCM）患者预后预测的重要指标；然而，由于缺乏标准化的测量，其临床应用受到阻碍。目的：建立并评价一种基于三维卷积神经网络（CNN）的HCM患者LGE疤痕自动量化方法。材料和方法：我们回顾性分析了2001年11月至2011年11月间一项多中心（n = 7）和多供应商（n = 3） HCM研究中的LGE MRI数据。采用基于U-Net架构的深度三维CNN对LGE疤痕进行量化。以4:1的比例维护独立的CNN训练和测试数据集。将短轴MRI切片分成重叠的子堆，然后分割并合并成一个卷。在核心实验室设置中，使用Dice相似系数（DSC）、Pearson相关性和Bland-Altman分析，对每个站点和每个供应商的3D CNN性能进行了评估。此外，将三维CNN与二维CNN的性能进行了比较。结果：本研究纳入1073例HCM患者（男性733例，平均年龄49岁+/- 17岁[标准差]）。基于3D cnn的量化速度快（每张图像0.15秒），与手动疤痕体积量化具有良好的相关性(r = 0.88， P
Background: Cardiac MRI late gadolinium enhancement (LGE) scar volume is an important marker for outcome prediction in patients with hypertrophic cardiomyopathy (HCM); however, its clinical application is hindered by a lack of measurement standardization.Purpose: To develop and evaluate a three-dimensional (3D) convolutional neural network (CNN)-based method for automated LGE scar quantification in patients with HCM.Materials and Methods: We retrospectively identified LGE MRI data in a multicenter (n = 7) and multivendor (n = 3) HCM study obtained between November 2001 and November 2011. A deep 3D CNN based on U-Net architecture was used for LGE scar quantification.Independent CNN training and testing data sets were maintained with a 4:1 ratio. Stacks of short-axis MRI slices were split into overlapping substacks that were segmented and then merged into one volume. The 3D CNN per-site and per-vendor performances were evaluated with respect to manual scar quantification performed in a core laboratory setting using Dice similarity coefficient (DSC), Pearson correlation, and Bland-Altman analyses. Furthermore, the performance of 3D CNN was compared with that of two-dimensional (2D) CNN.Results: This study included 1073 patients with HCM (733 men; mean age, 49 years +/- 17 [standard deviation]). The 3D CNN-based quantification was fast (0.15 second per image) and demonstrated excellent correlation with manual scar volume quantification(r = 0.88, P