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4 October 2024 Neural network-based virtual staining of defocused autofluorescence images of label-free tissue
Yijie Zhang, Luzhe Huang, Tairan Liu, Keyi Cheng, Kevin de Haan, Yuzhu Li, Bijie Bai, Aydogan Ozcan
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Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC1311808 (2024) https://doi.org/10.1117/12.3027365
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
We present a fast virtual-staining framework for defocused autofluorescence images of unlabeled tissue, matching the performance of standard virtual-staining models using in-focus label-free images. For this, we introduced a virtual-autofocusing network to digitally refocus the defocused images. Subsequently, these refocused images were transformed into virtually-stained H&E images using a successive neural network. Using coarsely-focused autofluorescence images, with 4-fold fewer focus points and 2-fold lower focusing precision, we achieved equivalent virtual-staining performance to standard H&E virtual-staining networks that utilize finely-focused images, helping us decrease the total image acquisition time by ~32% and the autofocusing time by ~89% for each whole-slide image.
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Yijie Zhang, Luzhe Huang, Tairan Liu, Keyi Cheng, Kevin de Haan, Yuzhu Li, Bijie Bai, and Aydogan Ozcan "Neural network-based virtual staining of defocused autofluorescence images of label-free tissue", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC1311808 (4 October 2024); https://doi.org/10.1117/12.3027365
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KEYWORDS
Nervous system
Autofluorescence
Neural networks
Education and training
Lung
Microscopes
Performance modeling
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