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7 March 2022 Improving the histological realism of ultraviolet photoacoustic remote sensing microscopy images using a deep learning-based generative adversarial network
Matthew T. Martell, Ewan A. McAlister, Nathaniel J. M. Haven, Sveta Silverman, Lashan Peiris, Jean Deschenes, Xingyu Li, Roger J. Zemp
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Proceedings Volume PC11960, Photons Plus Ultrasound: Imaging and Sensing 2022; PC1196013 (2022) https://doi.org/10.1117/12.2610461
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
Following resection of cancerous tissues, specimens are excised from the surgical margins to be examined post-operatively for the presence of residual cancer cells. Hematoxylin and eosin (H&E) staining is the gold standard of histopathological assessment. Ultraviolet photoacoustic microscopy (UV-PARS), combined with scattering microscopy, provides virtual nuclei and cytoplasm contrast similar to H&E staining. A generative adversarial network (GAN) deep learning approach, specifically a CycleGAN, was used to perform style transfer to improve the histological realism of UV-PARS generated images. Post-CycleGAN images are easier for a pathologist to examine and can be input into existing machine learning pipelines for H&E-stained images.
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Matthew T. Martell, Ewan A. McAlister, Nathaniel J. M. Haven, Sveta Silverman, Lashan Peiris, Jean Deschenes, Xingyu Li, and Roger J. Zemp "Improving the histological realism of ultraviolet photoacoustic remote sensing microscopy images using a deep learning-based generative adversarial network", Proc. SPIE PC11960, Photons Plus Ultrasound: Imaging and Sensing 2022, PC1196013 (7 March 2022); https://doi.org/10.1117/12.2610461
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KEYWORDS
Microscopy
Photoacoustic spectroscopy
Remote sensing
Ultraviolet radiation
Cancer
Oncology
Scattering
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