Predicting dark-field images of H and E-stained esophageal specimens

Berfin Arli; Omer Faruk Dinc; Merve Türker-Burhan; Serhat Tozburun

doi:10.1117/12.2672202

21 August 2023 Predicting dark-field images of H and E-stained esophageal specimens

Berfin Arli, Omer Faruk Dinc, Merve Türker-Burhan, Serhat Tozburun

Proceedings Volume 12630, Advances in Microscopic Imaging IV; 1263017 (2023) https://doi.org/10.1117/12.2672202
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

Abstract

The potential of laser-induced thermal therapy can be reassessed in treating abnormal mucosal tissues with advances in fiber optics, diode laser technology, and optical imaging modalities. In this context, studies optimizing a large parameter matrix (e.g., laser power, surface scanning speed, beam diameter, and irradiation duration) may be of interest. This study presents an artificial intelligence algorithm utilizing a generative adversarial network that predicts dark-field microscopy images from bright-field images of H&E-stained esophageal specimens. The calculated structural similarity index measurement between ground truth and the predicted dark-field image reaches an average of 74%. Also, the mean squared error is 0.7%.

Citation Download Citation

Berfin Arli, Omer Faruk Dinc, Merve Türker-Burhan, and Serhat Tozburun "Predicting dark-field images of H and E-stained esophageal specimens", Proc. SPIE 12630, Advances in Microscopic Imaging IV, 1263017 (21 August 2023); https://doi.org/10.1117/12.2672202

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