Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

翻译：组织学检查是尸检过程中的关键步骤；然而，传统尸检样本的组织化学染色面临多重挑战，包括因尸体组织固定延迟导致的自溶现象造成的染色质量下降，以及覆盖大面积组织的化学染色流程资源密集，需要大量人力、成本和时间。在全球卫生危机期间，当组织病理学服务可及性受限时，这些挑战可能更加突出，导致组织固定进一步延迟并产生更严重的染色伪影。本文首次报道了尸检组织的虚拟染色技术，证明经过训练的神经网络可以快速将无标记尸检组织切片的自动荧光图像转化为与同一样本苏木精-伊红（H&E）染色版本匹配的明场等效图像，从而消除传统尸检组织化学染色中因自溶引起的严重染色伪影。我们利用超过0.7 TB图像数据以及将虚拟染色网络与图像配准网络相结合的数据高效协作方案，训练了虚拟H&E模型。该训练模型在经历严重自溶的新尸检组织样本（例如从未见过的COVID-19样本）中有效突出了细胞核、细胞质和细胞外特征，而传统组织化学染色无法为这些样本提供一致的染色质量。这种虚拟尸检染色技术还可扩展应用于坏死组织，且能在严重自溶和细胞死亡的情况下快速、经济地生成无伪影H&E染色，同时减少标准组织化学染色相关的人力、成本和基础设施需求。