Light: Science & Applications ( IF 19.4 ) Pub Date : 2021-09-01 , DOI: 10.1038/s41377-021-00620-8 Neha Goswami 1, 2 , Yuchen R He 2, 3 , Yu-Heng Deng 4 , Chamteut Oh 5 , Nahil Sobh 2, 6 , Enrique Valera 1, 7, 8 , Rashid Bashir 1, 7, 8, 9, 10 , Nahed Ismail 11 , Hyunjoon Kong 2, 4 , Thanh H Nguyen 5, 9 , Catherine Best-Popescu 1, 2 , Gabriel Popescu 1, 2, 3
Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically.
中文翻译:
使用具有计算特异性的相位成像进行无标记 SARS-CoV-2 检测和分类
缓解 COVID-19 危机的努力表明,快速、准确和可扩展的测试对于遏制当前和未来大流行的影响至关重要。我们提出了一种直接对未标记病毒颗粒进行成像并使用深度学习进行检测和分类的光学方法。使用超灵敏干涉测量法对四种具有纳米级光路长度灵敏度的病毒类型进行成像。将这些数据与用于地面实况的荧光图像配对,我们训练了基于 U-Net(一种特殊类型的卷积神经网络)的语义分割模型。训练后的网络仅用于对干涉图像中的病毒进行分类,同时包含 SARS-CoV-2、H1N1(甲型流感病毒)、HAdV(腺病毒)和 ZIKV(寨卡病毒)。值得注意的是,由于输入数据的纳米级灵敏度,神经网络能够以 96% 的准确率识别 SARS-CoV-2 与其他病毒。每个图像的推理时间为 60 毫秒,在通用图形处理单元上。这种对未标记病毒颗粒进行直接成像的方法可以提供极快的测试,每个患者不到一分钟。由于成像仪器在常规载玻片上运行,我们设想这种方法可能对患者呼吸冷凝物进行测试。必要的高通量可以通过从数字病理学中转换概念来实现,其中显微镜可以自动扫描数百张载玻片。每位患者不到一分钟。由于成像仪器在常规载玻片上运行,我们设想这种方法可能对患者呼吸冷凝物进行测试。必要的高通量可以通过从数字病理学中转换概念来实现,其中显微镜可以自动扫描数百张载玻片。每位患者不到一分钟。由于成像仪器在常规载玻片上运行,我们设想这种方法可能对患者呼吸冷凝物进行测试。必要的高通量可以通过从数字病理学中转换概念来实现,其中显微镜可以自动扫描数百张载玻片。