CycleGAN for Reflection and Aberration Artifact Removal in Quantitative Phase Images of Mesenchymal Stromal Cells

Zixin Yong; Elizabeth Lee; Maciej Baranski; Lisa Tucker-Kellogg; Lisa Tucker-Kellogg; George Barbastathis; George Barbastathis

doi:10.1364/NTM.2021.NM1C.3

Biophotonics Congress 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper NM1C.3
•https://doi.org/10.1364/NTM.2021.NM1C.3

CycleGAN for Reflection and Aberration Artifact Removal in Quantitative Phase Images of Mesenchymal Stromal Cells

Zixin Yong, Elizabeth Lee, Maciej Baranski, Lisa Tucker-Kellogg, and George Barbastathis

Novel Techniques in Microscopy 2021

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

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Deep Learning (NM1C)

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Z. Yong, E. Lee, M. Baranski, L. Tucker-Kellogg, and G. Barbastathis, "CycleGAN for Reflection and Aberration Artifact Removal in Quantitative Phase Images of Mesenchymal Stromal Cells," 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 NM1C.3.

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Abstract

We demonstrate artifact removal in quantitative phase images of cells using a cycle-consistent generative adversarial network (cycleGAN). This method relaxes the requirement on the optical quality of the imaging vessel even without having paired data.

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