Virtual H&E staining with single channel reflectance confocal microscopy images using pixel-to-pixel-based deep learning

Mengkun Chen; Matthew C. Fox; Jason S. Reichenberg; Fabiana C. P. S. Lopes; Katherine R. Sebastian; Mia K. Markey; James W. Tunnell

doi:10.1117/12.3001974

12 March 2024 Virtual H&E staining with single channel reflectance confocal microscopy images using pixel-to-pixel-based deep learning

Mengkun Chen, Matthew C. Fox, Jason S. Reichenberg, Fabiana C. P. S. Lopes, Katherine R. Sebastian, Mia K. Markey, James W. Tunnell

Author Affiliations +

Proceedings Volume 12831, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXII; 1283105 (2024) https://doi.org/10.1117/12.3001974
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Virtual staining creates H&E-like images with minimal tissue processing. Typically, two channels are used, but single-channel staining is attractive for techniques like reflectance confocal microscopy (RCM). Our study trains a deep learning model to generate H&E images from single-channel RCM using pixel-level registration. Porcine skin was stained with acridine orange, SR101, and aluminum chloride, and confocal microscopy images were acquired. Using pix2pixGAN, we trained the model on grayscale RCM images, producing virtual stained images that closely resembled the ground truth. We showed some model output examples and used image assessment metrics to evaluate model performance. This technique has potential for in vivo surgical applications, eliminating the need for image registration.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengkun Chen, Matthew C. Fox, Jason S. Reichenberg, Fabiana C. P. S. Lopes, Katherine R. Sebastian, Mia K. Markey, and James W. Tunnell "Virtual H&E staining with single channel reflectance confocal microscopy images using pixel-to-pixel-based deep learning", Proc. SPIE 12831, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXII, 1283105 (12 March 2024); https://doi.org/10.1117/12.3001974

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