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Virtual Staining of Defocused Autofluorescence Images of Unlabeled Tissue Using Deep Neural Networks

自体荧光 人工智能 染色 计算机科学 计算机视觉 污渍 光学(聚焦) 图像质量 生物医学工程 图像(数学) 病理 光学 医学 物理 荧光
作者
Yijie Zhang,Luzhe Huang,Tairan Liu,Keyi Cheng,Kevin de Haan,Yuzhu Li,Bijie Bai,Aydogan Özcan
标识
DOI:10.34133/2022/9818965
摘要

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.
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