Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury.
Deep Neural Network-Aided Histopathological Analysis of Myocardial Injury.
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DOI：
10.3389/fcvm.2021.724183
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发表时间：
2021
影响因子：
3.6
通讯作者：
Ding Y
中科院分区：
文献类型：
--
作者：
Jiao Y;Yuan J;Sodimu OM;Qiang Y;Ding Y
深度神经网络已成为分析和解释组织学图像的主流方法。在这项研究中，我们建立并验证了一个可解释的DNN模型，以评估心肌损伤患者的子宫内膜活检（EMB）数据。使用深度学习模型提取特征并对诊断为缺血性心肌病或特发性扩张型心肌病的心力衰竭病例和非衰竭病例（无心力衰竭史的器官供体）的EMB组织病理学图像进行分类。我们利用梯度加权类激活映射（Grad-CAM）技术，强调受伤的地区，提供一个切入点，以评估的过程中，一个全面的评估占主导地位的形态。为了可视化聚集的感兴趣区域（ROI），我们利用均匀流形近似和投影（UMAP）嵌入降维。我们进一步实现了多模型集成机制，以将ROI水平和病例水平的定量指标（受试者工作特征曲线下面积，AUC）分别提高到0.985和0.992，优于基于子模型的0.971 ± 0.017和0.981 ± 0.020。总的来说，这种新的方法提供了一个强大的和解释性的框架，以探索当地的组织病理学模式，促进自动和高通量定量心脏EMB分析。
Deep neural networks have become the mainstream approach for analyzing and interpreting histology images. In this study, we established and validated an interpretable DNN model to assess endomyocardial biopsy (EMB) data of patients with myocardial injury. Deep learning models were used to extract features and classify EMB histopathological images of heart failure cases diagnosed with either ischemic cardiomyopathy or idiopathic dilated cardiomyopathy and non-failing cases (organ donors without a history of heart failure). We utilized the gradient-weighted class activation mapping (Grad-CAM) technique to emphasize injured regions, providing an entry point to assess the dominant morphology in the process of a comprehensive evaluation. To visualize clustered regions of interest (ROI), we utilized uniform manifold approximation and projection (UMAP) embedding for dimension reduction. We further implemented a multi-model ensemble mechanism to improve the quantitative metric (area under the receiver operating characteristic curve, AUC) to 0.985 and 0.992 on ROI-level and case-level, respectively, outperforming the achievement of 0.971 ± 0.017 and 0.981 ± 0.020 based on the sub-models. Collectively, this new methodology provides a robust and interpretive framework to explore local histopathological patterns, facilitating the automatic and high-throughput quantification of cardiac EMB analysis.