Devan K Shaga, Walther P, von Einem J, Ropinski T, Kestler H A, Read C
Central Facility for Electron Microscopy, Ulm University, Ulm, Germany.
Institute of Virology, Ulm University Medical Center, Ulm, Germany.
Histochem Cell Biol. 2019 Feb;151(2):101-114. doi: 10.1007/s00418-018-1759-5. Epub 2018 Nov 28.
The detailed analysis of secondary envelopment of the Human betaherpesvirus 5/human cytomegalovirus (HCMV) from transmission electron microscopy (TEM) images is an important step towards understanding the mechanisms underlying the formation of infectious virions. As a step towards a software-based quantification of different stages of HCMV virion morphogenesis in TEM, we developed a transfer learning approach based on convolutional neural networks (CNNs) that automatically detects HCMV nucleocapsids in TEM images. In contrast to existing image analysis techniques that require time-consuming manual definition of structural features, our method automatically learns discriminative features from raw images without the need for extensive pre-processing. For this a constantly growing TEM image database of HCMV infected cells was available which is unique regarding image quality and size in the terms of virological EM. From the two investigated types of transfer learning approaches, namely feature extraction and fine-tuning, the latter enabled us to successfully detect HCMV nucleocapsids in TEM images. Our detection method has outperformed some of the existing image analysis methods based on discriminative textural indicators and radial density profiles for virus detection in TEM images. In summary, we could show that the method of transfer learning can be used for an automated detection of viral capsids in TEM images with high specificity using standard computers. This method is highly adaptable and in future could be easily extended to automatically detect and classify virions of other viruses and even distinguish different virion maturation stages.
从透射电子显微镜（TEM）图像中对人β疱疹病毒5/人巨细胞病毒（HCMV）的二次包膜进行详细分析，是理解传染性病毒粒子形成机制的重要一步。作为迈向基于软件对TEM中HCMV病毒粒子形态发生不同阶段进行定量分析的一步，我们开发了一种基于卷积神经网络（CNN）的迁移学习方法，该方法可自动检测TEM图像中的HCMV核衣壳。与现有的需要耗时手动定义结构特征的图像分析技术不同，我们的方法无需大量预处理即可从原始图像中自动学习判别特征。为此，有一个不断增长的HCMV感染细胞的TEM图像数据库，就病毒学电子显微镜的图像质量和大小而言，该数据库是独一无二的。在研究的两种迁移学习方法中，即特征提取和微调，后者使我们能够成功检测TEM图像中的HCMV核衣壳。我们的检测方法在基于判别纹理指标和径向密度分布的TEM图像病毒检测方面优于一些现有的图像分析方法。总之，我们可以证明，迁移学习方法可用于使用标准计算机以高特异性自动检测TEM图像中的病毒衣壳。该方法具有高度适应性，未来可以轻松扩展以自动检测和分类其他病毒的病毒粒子，甚至区分不同的病毒粒子成熟阶段。
