Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning

Sunil Kumar Gaire; Ethan Flowerday; Jane Frederick; Ruyi Gong; Leslie Ying; Hao F. Zhang; Vadim Backman

doi:10.1364/ISA.2023.IM4E.4

Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning

Sunil Kumar Gaire
, Ethan Flowerday
, Jane Frederick
, Ruyi Gong
, Leslie Ying
, Hao F. Zhang
, Vadim Backman

Department of Biomedical Engineering

North Carolina A&T State University
Northwestern University

Research output: Contribution to conference › Paper › peer-review

Abstract

We present all machine learning-based reconstruction approach for simultaneous multicolor spectroscopic super-resolution microscopy. Machine learning is used for localization and spectral classification. Reconstruction of the simultaneous two-color imaging of the nanoruler sample with a mark-to-mark distance of 40 nm is presented.

Original language	English
DOIs	https://doi.org/10.1364/ISA.2023.IM4E.4
State	Published - 2023
Event	2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023 - Boston, United States Duration: Aug 14 2023 → Aug 17 2023

Conference

Conference	2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023
Country/Territory	United States
City	Boston
Period	08/14/23 → 08/17/23

Access to Document

10.1364/ISA.2023.IM4E.4

Cite this

Gaire, S. K., Flowerday, E., Frederick, J., Gong, R., Ying, L., Zhang, H. F., & Backman, V. (2023). Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning. Paper presented at 2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023, Boston, United States. https://doi.org/10.1364/ISA.2023.IM4E.4

@conference{a751a125aad447d1aeb930da7407059b,

title = "Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning",

abstract = "We present all machine learning-based reconstruction approach for simultaneous multicolor spectroscopic super-resolution microscopy. Machine learning is used for localization and spectral classification. Reconstruction of the simultaneous two-color imaging of the nanoruler sample with a mark-to-mark distance of 40 nm is presented.",

author = "Gaire, \{Sunil Kumar\} and Ethan Flowerday and Jane Frederick and Ruyi Gong and Leslie Ying and Zhang, \{Hao F.\} and Vadim Backman",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s).; 2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023 ; Conference date: 14-08-2023 Through 17-08-2023",

year = "2023",

doi = "10.1364/ISA.2023.IM4E.4",

language = "English",

}

Gaire, SK, Flowerday, E, Frederick, J, Gong, R, Ying, L, Zhang, HF & Backman, V 2023, 'Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning', Paper presented at 2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023, Boston, United States, 08/14/23 - 08/17/23. https://doi.org/10.1364/ISA.2023.IM4E.4

Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning. / Gaire, Sunil Kumar; Flowerday, Ethan; Frederick, Jane et al.
2023. Paper presented at 2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023, Boston, United States.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning

AU - Gaire, Sunil Kumar

AU - Flowerday, Ethan

AU - Frederick, Jane

AU - Gong, Ruyi

AU - Ying, Leslie

AU - Zhang, Hao F.

AU - Backman, Vadim

PY - 2023

Y1 - 2023

N2 - We present all machine learning-based reconstruction approach for simultaneous multicolor spectroscopic super-resolution microscopy. Machine learning is used for localization and spectral classification. Reconstruction of the simultaneous two-color imaging of the nanoruler sample with a mark-to-mark distance of 40 nm is presented.

AB - We present all machine learning-based reconstruction approach for simultaneous multicolor spectroscopic super-resolution microscopy. Machine learning is used for localization and spectral classification. Reconstruction of the simultaneous two-color imaging of the nanoruler sample with a mark-to-mark distance of 40 nm is presented.

UR - https://www.scopus.com/pages/publications/85193038309

U2 - 10.1364/ISA.2023.IM4E.4

DO - 10.1364/ISA.2023.IM4E.4

M3 - Paper

AN - SCOPUS:85193038309

T2 - 2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

Y2 - 14 August 2023 through 17 August 2023

ER -

Gaire SK, Flowerday E, Frederick J, Gong R, Ying L, Zhang HF et al.. Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning. 2023. Paper presented at 2023 Imaging Systems and Applications, IS 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023, Boston, United States. doi: 10.1364/ISA.2023.IM4E.4

Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning

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