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.

通过虚拟染色技术，我们报告了尸检组织的首次实证，并展示了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相同样品的苏木精和伊红 (HE) 染色版本相匹配的亮场等效图像，消除了传统组织化学染色中固有的由于尸体组织延迟固定引起的严重染色伪影，使其可以在新的经历严重自溶的尸检组织样品中有效地强调细胞核、细胞质和细胞外特征，该技术还可应用于坏死组织，能够快速且具有低成本地生成无伪影的 HE 染色，同时减少了与标准组织化学染色相关的劳动、成本和基础设施需求。

无标记尸检组织的虚拟组织学染色

Virtual histological staining of unlabeled autopsy tissue

MUSE is a novel slide-free imaging technique for histological examination of
tissues that can serve as an alternative to traditional histology. In order to
bridge the gap between MUSE and traditional histology, we aim to convert MUSE
images to resemble authentic hematoxylin- and eosin-stained (H&E) images. We
evaluated four models: a non-machine-learning-based color-mapping
unmixing-based tool, CycleGAN, DualGAN, and GANILLA. CycleGAN and GANILLA
provided visually compelling results that appropriately transferred H&E style
and preserved MUSE content. Based on training an automated critic on real and
generated H&E images, we determined that CycleGAN demonstrated the best
performance. We have also found that MUSE color inversion may be a necessary
step for accurate modality conversion to H&E. We believe that our MUSE-to-H&E
model can help improve adoption of novel slide-free methods by bridging a
perceptual gap between MUSE imaging and traditional histology.

通过评估 4 个模型，我们发现 CycleGAN 工具在 MUSE 到 H＆E 转换中表现最佳，还发现 MUSE 颜色反转对于准确的模态转换可能是必要的。