Deep-learning-enabled polarization-sensitive optical coherence tomography (OCT)

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

Deep-learning-enabled polarization-sensitive optical coherence tomography (OCT)

Yi Sun^*, Jianfeng Wang, Jindou Shi, Stephen A. Boppart^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Article number	OF2E.3
Journal	Optics InfoBase Conference Papers
Publication status	Published - 2021
Externally published	Yes
Event	Optical Molecular Probes, Imaging and Drug Delivery, OMP 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021 - Virtual, Online, United States Duration: 12 Apr 2021 → 16 Apr 2021

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title = "Deep-learning-enabled polarization-sensitive optical coherence tomography (OCT)",

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.",

author = "Yi Sun and Jianfeng Wang and Jindou Shi and Boppart, {Stephen A.}",

note = "Publisher Copyright: {\textcopyright} 2021 The Author (s).; Optical Molecular Probes, Imaging and Drug Delivery, OMP 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021 ; Conference date: 12-04-2021 Through 16-04-2021",

year = "2021",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

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TY - JOUR

T1 - Deep-learning-enabled polarization-sensitive optical coherence tomography (OCT)

AU - Sun, Yi

AU - Wang, Jianfeng

AU - Shi, Jindou

AU - Boppart, Stephen A.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85120566778&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85120566778

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - OF2E.3

T2 - Optical Molecular Probes, Imaging and Drug Delivery, OMP 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

Y2 - 12 April 2021 through 16 April 2021

ER -

Deep-learning-enabled polarization-sensitive optical coherence tomography (OCT)

Abstract

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