5 March 2021Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Christopher M. Polleys, Panagiotis Lymperopoulos, Hong-Thao Thieu, Elizabeth Genega, Liping Liu, 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