Deep-Learning-Enabled Polarization-Sensitive Optical Coherence Tomography (OCT)

Yi Sun; Yi Sun; Jianfeng Wang; Jindou Shi; Jindou Shi; Stephen A Boppart; Stephen A Boppart

doi:10.1364/OMP.2021.OF2E.3

Biophotonics Congress 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper OF2E.3
•https://doi.org/10.1364/OMP.2021.OF2E.3

Deep-Learning-Enabled Polarization-Sensitive Optical Coherence Tomography (OCT)

Yi Sun, Jianfeng Wang, Jindou Shi, and Stephen A Boppart

Optical Molecular Probes, Imaging and Drug Delivery 2021

Washington, DC United States
12–16 April 2021
ISBN: 978-1-943580-85-9

From the session
Tissue Morphology (OF2E)

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Y. Sun, J. Wang, J. Shi, and S. A. Boppart, "Deep-Learning-Enabled Polarization-Sensitive Optical Coherence Tomography (OCT)," in Biophotonics Congress 2021, C. Boudoux, K. Maitland, C. Hendon, M. Wojtkowski, K. Quinn, M. Schanne-Klein, N. Durr, D. Elson, F. Cichos, L. Oddershede, V. Emiliani, O. Maragò, S. Nic Chormaic, N. Pégard, S. Gibbs, S. Vinogradov, M. Niedre, K. Samkoe, A. Devor, D. Peterka, P. Blinder, and E. Buckley, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper OF2E.3.

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Abstract

A trained deep neural network generates computational polarization-sensitive contrast from OCT intensity images. The deep-learning-enabled images were evaluated based on the structural similarity with the ground truth.

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