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6 March 2023 Virtual stain-to-stain transformations via cascaded deep neural networks
Xilin Yang, Bijie Bai, Yuzhu Li, Yijie Zhang, Tairan Liu, Kevin de Haan, Aydogan Ozcan
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Proceedings Volume PC12371, Multimodal Biomedical Imaging XVIII; PC123710C (2023) https://doi.org/10.1117/12.2648101
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
We present a deep learning-based framework to virtually transfer images of H&E-stained tissue to other stain types using cascaded deep neural networks. This method, termed C-DNN, was trained in a cascaded manner: label-free autofluorescence images were fed to the first generator as input and transformed into H&E stained images. These virtually stained H&E images were then transformed into Periodic acid–Schiff (PAS) stain by the second generator. We trained and tested C-DNN on kidney needle-core biopsy tissue, and its output images showed better color accuracy and higher contrast on various histological features compared to other stain transfer models.
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Xilin Yang, Bijie Bai, Yuzhu Li, Yijie Zhang, Tairan Liu, Kevin de Haan, and Aydogan Ozcan "Virtual stain-to-stain transformations via cascaded deep neural networks", Proc. SPIE PC12371, Multimodal Biomedical Imaging XVIII, PC123710C (6 March 2023); https://doi.org/10.1117/12.2648101
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KEYWORDS
Neural networks
Tissues
Data modeling
Biopsy
Diagnostics
Image quality
Kidney
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