Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues

Christopher M. Polleys; Panagiotis Lymperopoulos; Hong-Thao Thieu; Elizabeth Genega; Liping Liu; Irene Georgakoudi

doi:10.1117/12.2578650

5 March 2021 Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues

Christopher M. Polleys, Panagiotis Lymperopoulos, Hong-Thao Thieu, Elizabeth Genega, Liping Liu, Irene Georgakoudi

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Proceedings Volume 11647, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX; 116470Z (2021) https://doi.org/10.1117/12.2578650
Event: SPIE BiOS, 2021, Online Only

Abstract

High signal-to-noise ratio (SNR) images are necessary for analyzing sub-cellular features in biomedical images. Acquisition of such images may be limited by temporal or photon-budget-based imaging constraints. This study aims to use deep-learning-based image restoration methods to extract morpho-functional information from low-SNR, depth-resolved, label-free, two-photon images of human cervical tissue. A deep convolutional autoencoder model was trained using single-frame image inputs and multiple-frame averaged ground-truth image pairs. Automated analysis of restored reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) two-photon excitation fluorescence (TPEF) images extracts depth-dependent, morpho-functional information otherwise lost in single-frame images.

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Christopher M. Polleys, Panagiotis Lymperopoulos, Hong-Thao Thieu, Elizabeth Genega, Liping Liu, and Irene Georgakoudi "Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues", Proc. SPIE 11647, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX, 116470Z (5 March 2021); https://doi.org/10.1117/12.2578650

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KEYWORDS

Image restoration

Tissues

Signal to noise ratio

In vivo imaging

Biopsy

Image quality

Imaging systems

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