Deep learning-enabled virtual staining of label-free autopsy tissue sections

Yuzhu Li; Nir Pillar; Jingxi Li; Tairan Liu; Di Wu; Songyu Sun; Guangdong Ma; Kevin de Haan; Luzhe Huang; Yijie Zhang; Sepehr Hamidi; Anatoly Urisman; Tal K. Haran; W. Dean Wallace; Jonathan E. Zuckerman; Aydogan Ozcan

doi:10.1117/12.3027873

4 October 2024 Deep learning-enabled virtual staining of label-free autopsy tissue sections

Yuzhu Li, Nir Pillar, Jingxi Li, Tairan Liu, Di Wu, Songyu Sun, Guangdong Ma, Kevin de Haan, Luzhe Huang, Yijie Zhang, Sepehr Hamidi, Anatoly Urisman, Tal K. Haran, W. Dean Wallace, Jonathan E. Zuckerman, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC131180X (2024) https://doi.org/10.1117/12.3027873
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

The traditional histochemical staining of autopsy tissue samples usually suffers from staining artifacts due to autolysis caused by delayed fixation of cadaver tissues. Here, we introduce an autopsy virtual staining technique to digitally convert autofluorescence images of unlabeled autopsy tissue sections into their hematoxylin and eosin (H&E) stained counterparts through a trained neural network. This technique was demonstrated to effectively mitigate autolysis-induced artifacts inherent in histochemical staining, such as weak nuclear contrast and color fading in the cytoplasmic-extracellular matrix. As a rapid, reagent-efficient, and high-quality histological staining approach, the presented technique holds great potential for widespread application in the future.

Conference Presentation

Citation Download Citation

Yuzhu Li, Nir Pillar, Jingxi Li, Tairan Liu, Di Wu, Songyu Sun, Guangdong Ma, Kevin de Haan, Luzhe Huang, Yijie Zhang, Sepehr Hamidi, Anatoly Urisman, Tal K. Haran, W. Dean Wallace, Jonathan E. Zuckerman, and Aydogan Ozcan "Deep learning-enabled virtual staining of label-free autopsy tissue sections", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC131180X (4 October 2024); https://doi.org/10.1117/12.3027873

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