Contrast improvement through a Generative Adversarial Network (GAN) by utilizing a dataset obtained from a line-scanning confocal microscope.

Amir Mohammad Ketabchi; Berna Morova; Nima Bavili; Alper Kiraz

doi:10.1117/12.3016368

18 June 2024 Contrast improvement through a Generative Adversarial Network (GAN) by utilizing a dataset obtained from a line-scanning confocal microscope.

Amir Mohammad Ketabchi, Berna Morova, Nima Bavili, Alper Kiraz

Author Affiliations +

Proceedings Volume 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII; 129980M (2024) https://doi.org/10.1117/12.3016368
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Confocal microscopy offers enhanced image contrast and signal-to-noise ratio compared to wide-field illumination microscopy, achieved by effectively eliminating out-of-focus background noise. In our study, we initially showcase the functionality of a line-scanning confocal microscope aligned through the utilization of a Digital Light Projector (DLP) and a rolling shutter CMOS camera. In this technique, a sequence of illumination lines is projected onto a sample using a DLP and focusing objective (50X, NA=0.55). The reflected light is imaged with the camera. Line-scanning confocal imaging is accomplished by synchronizing the illumination lines with the rolling shutter of the sensor, leading to a substantial enhancement of approximately 50% in image contrast. Subsequently, this setup is employed to create a dataset comprising 500 pairs of images of paper tissue. This dataset is employed for training a Generative Adversarial Network (cGAN). Roughly 45% contrast improvement was measured in the test images for the trained network, in comparison to the ground-truth images.

Conference Presentation

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

Citation Download Citation

Amir Mohammad Ketabchi, Berna Morova, Nima Bavili, and Alper Kiraz "Contrast improvement through a Generative Adversarial Network (GAN) by utilizing a dataset obtained from a line-scanning confocal microscope.", Proc. SPIE 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII, 129980M (18 June 2024); https://doi.org/10.1117/12.3016368

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