Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks
Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks
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DOI：
10.34133/2022/9818965
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发表时间：
2022-07
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通讯作者：
Yijie Zhang;Luzhe Huang;Tairan Liu;Keyi Cheng;K. Haan;Yuzhu Li;Bijie Bai;Aydogan Ozcan
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作者：
Yijie Zhang;Luzhe Huang;Tairan Liu;Keyi Cheng;K. Haan;Yuzhu Li;Bijie Bai;Aydogan Ozcan
提出了一种基于深度学习的虚拟染色方法，将图像对比度引入到无标记的组织切片中，与耗时、费力且对组织具有破坏性的组织学染色进行数字匹配。标准的虚拟染色在无标记组织的整个玻片成像过程中需要很高的自动聚焦精度，这会消耗总成像时间的很大一部分，并可能导致组织光损伤。在这里，我们介绍了一种快速的虚拟染色框架，它可以对未标记组织的散焦自发荧光图像进行染色，实现了与聚焦无标记图像的虚拟染色相同的性能，并通过降低显微镜的自动对焦精度节省了大量的成像时间。该框架结合了一个虚拟自动聚焦神经网络来对散焦图像进行数字重新聚焦，然后使用连续的网络将重新聚焦的图像转换成虚拟的彩色图像。这些级联网络形成了一种协作推理方案：虚拟染色模型通过训练过程中的风格损失来规则化虚拟自动聚焦网络。为了证明这个框架的有效性，我们使用人类肺组织对这些网络进行了训练和盲目测试。使用4×更少的焦点和2×更低的聚焦精度，我们成功地将粗聚焦的自发荧光图像转换为高质量的虚拟染色的H&E图像，与使用精细聚焦的自发荧光输入图像的标准虚拟染色框架相匹配。在不牺牲染色质量的情况下，该框架将无标记全片图像(WSI)虚拟染色所需的总图像采集时间减少了约32%，自动对焦时间减少了约89%，并有可能消除病理中繁琐而昂贵的组织化学染色过程。
Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining requires high autofocusing precision during the whole slide imaging of label-free tissue, which consumes a significant portion of the total imaging time and can lead to tissue photodamage. Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope’s autofocusing precision. This framework incorporates a virtual autofocusing neural network to digitally refocus the defocused images and then transforms the refocused images into virtually stained images using a successive network. These cascaded networks form a collaborative inference scheme: the virtual staining model regularizes the virtual autofocusing network through a style loss during the training. To demonstrate the efficacy of this framework, we trained and blindly tested these networks using human lung tissue. Using 4× fewer focus points with 2× lower focusing precision, we successfully transformed the coarsely-focused autofluorescence images into high-quality virtually stained H&E images, matching the standard virtual staining framework that used finely-focused autofluorescence input images. Without sacrificing the staining quality, this framework decreases the total image acquisition time needed for virtual staining of a label-free whole-slide image (WSI) by ~32%, together with a ~89% decrease in the autofocusing time, and has the potential to eliminate the laborious and costly histochemical staining process in pathology.