Rey Juan S, Li Wen, Bryer Alexander J, Beatson Hagan, Lantz Christian, Engelman Alan N, Perilla Juan R
Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States.
Department of Medicine, Harvard Medical School, Boston, MA, United States.
Comput Struct Biotechnol J. 2021 Oct 5;19:5688-5700. doi: 10.1016/j.csbj.2021.10.001. eCollection 2021.
Transmission electron microscopy (TEM) has a multitude of uses in biomedical imaging due to its ability to discern ultrastructure morphology at the nanometer scale. Through its ability to directly visualize virus particles, TEM has for several decades been an invaluable tool in the virologist's toolbox. As applied to HIV-1 research, TEM is critical to evaluate activities of inhibitors that block the maturation and morphogenesis steps of the virus lifecycle. However, both the preparation and analysis of TEM micrographs requires time consuming manual labor. Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed a convolutional neural network backbone of a two-stage Region Based Convolutional Neural Network (RCNN) capable of identifying, segmenting and classifying HIV-1 virions at different stages of maturation and morphogenesis. Our results outperformed common RCNN backbones, achieving 80.0% mean Average Precision on a diverse set of micrographs comprising different experimental samples and magnifications. We expect that this tool will be of interest to a broad range of researchers.
透射电子显微镜（TEM）在生物医学成像中有多种用途，因为它能够在纳米尺度上辨别超微结构形态。由于能够直接观察病毒颗粒，几十年来，TEM一直是病毒学家工具箱中不可或缺的工具。在应用于HIV-1研究时，TEM对于评估阻断病毒生命周期成熟和形态发生步骤的抑制剂的活性至关重要。然而，TEM显微照片的制备和分析都需要耗时的人工操作。通过专门使用计算机视觉框架和机器学习技术，我们开发了一种基于两阶段区域卷积神经网络（RCNN）的卷积神经网络主干，能够识别、分割和分类处于不同成熟和形态发生阶段的HIV-1病毒粒子。我们的结果优于常见的RCNN主干，在包含不同实验样本和放大倍数的各种显微照片上实现了80.0%的平均精度。我们预计这个工具将受到广泛研究人员的关注。
