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6 March 2023 Deep learning-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 PC12373, Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis; PC123730H (2023) https://doi.org/10.1117/12.2648092
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
We present a virtual staining framework that can rapidly stain defocused autofluorescence images of label-free tissue, matching the performance of standard virtual staining models that use in-focus unlabeled images. We trained and blindly tested this deep learning-based framework using human lung tissue. Using coarsely-focused autofluorescence images acquired with 4× fewer focus points and 2× lower focusing precision, we achieved equivalent performance to the standard virtual staining that used finely-focused autofluorescence input images. We achieved a ~32% decrease in the total image acquisition time needed for virtual staining of a label-free whole-slide image, alongside a ~89% decrease in the autofocusing time.
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Yijie Zhang, Luzhe Huang, Tairan Liu, Keyi Cheng, Kevin de Haan, Yuzhu Li, Bijie Bai, and Aydogan Ozcan "Deep learning-based virtual staining of defocused autofluorescence images of label-free tissue ", Proc. SPIE PC12373, Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis, PC123730H (6 March 2023); https://doi.org/10.1117/12.2648092
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KEYWORDS
Tissues
Neural networks
Image acquisition
Lung
Performance modeling
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