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 4x fewer focus points with 2x 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.
翻译:开发了基于深层学习的虚拟污点,以引入与无标签组织部分的图像对比的图像,在数字上匹配无标签组织图层污点,这需要花费时间、劳动密集型和破坏组织。标准虚拟污点在无标签组织整个幻灯片成像过程中需要高自动聚焦精确度。标准虚拟污点在无标签组织整个幻灯片成像过程中需要高自动聚焦度的神经网络,这耗尽了大部分成像成像成像成像时间,这可以导致组织照片损坏。在这里,我们引入了一个快速虚拟的虚拟污点框架,可以污点突出无标签组织中的自动浮质图像,实现与无标签组织图像的虚拟污点相当的性能,同时通过降低显像标度图像的虚拟污点,我们培训和盲目的测试这些网络的总成像时段的精确度精确度,然后通过连续的网络将重心部图像转换为高清晰度图点, 使用4x的下等价点,我们用低的标点将标度标度标度标度标度标度标度标度标度标度标度标度标定整个框架 降低成本 。