Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images

Research output: Working paper › Preprint › Research

Standard

Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images. / Netterstrøm, Rasmus; Kutuzov, Nikolay; Darkner, Sune; Pallesen, Maurits Jørring; Lauritzen, Martin; Erleben, Kenny; Lauze, Francois Bernard.

arXiv.org, 2023.

Research output: Working paper › Preprint › Research

Harvard

Netterstrøm, R, Kutuzov, N, Darkner, S, Pallesen, MJ, Lauritzen, M, Erleben, K & Lauze, FB 2023 'Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images' arXiv.org. <https://arxiv.org/abs/2303.16903>

APA

Netterstrøm, R., Kutuzov, N., Darkner, S., Pallesen, M. J., Lauritzen, M., Erleben, K., & Lauze, F. B. (2023). Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images. arXiv.org. https://arxiv.org/abs/2303.16903

Vancouver

Netterstrøm R, Kutuzov N, Darkner S, Pallesen MJ, Lauritzen M, Erleben K et al. Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images. arXiv.org. 2023.

Author

Netterstrøm, Rasmus ; Kutuzov, Nikolay ; Darkner, Sune ; Pallesen, Maurits Jørring ; Lauritzen, Martin ; Erleben, Kenny ; Lauze, Francois Bernard. / Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images. arXiv.org, 2023.

Bibtex

@techreport{9f1fa90f57ac467d8d88fb054d6a26b2,
title = "Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images",
abstract = "Tracking single molecules is instrumental for quantifying the transport of molecules and nanoparticles in biological samples, e.g., in brain drug delivery studies. Existing intensity-based localisation methods are not developed for imaging with a scanning microscope, typically used for in vivo imaging. Low signal-to-noise ratios, movement of molecules out-of-focus, and high motion blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose a challenge to the accurate localisation of molecules. Using data-driven models is challenging due to low data volumes, typical for in vivo experiments. We developed a 2PM image simulator to supplement scarce training data. The simulator mimics realistic motion blur, background fluorescence, and shot noise observed in vivo imaging. Training a data-driven model with simulated data improves localisation quality in simulated images and shows why intensity-based methods fail.",
author = "Rasmus Netterstr{\o}m and Nikolay Kutuzov and Sune Darkner and Pallesen, {Maurits J{\o}rring} and Martin Lauritzen and Kenny Erleben and Lauze, {Francois Bernard}",
year = "2023",
language = "English",
publisher = "arXiv.org",
type = "WorkingPaper",
institution = "arXiv.org",

}

RIS

TY - UNPB

T1 - Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images

AU - Netterstrøm, Rasmus

AU - Kutuzov, Nikolay

AU - Darkner, Sune

AU - Pallesen, Maurits Jørring

AU - Lauritzen, Martin

AU - Erleben, Kenny

AU - Lauze, Francois Bernard

PY - 2023

Y1 - 2023

N2 - Tracking single molecules is instrumental for quantifying the transport of molecules and nanoparticles in biological samples, e.g., in brain drug delivery studies. Existing intensity-based localisation methods are not developed for imaging with a scanning microscope, typically used for in vivo imaging. Low signal-to-noise ratios, movement of molecules out-of-focus, and high motion blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose a challenge to the accurate localisation of molecules. Using data-driven models is challenging due to low data volumes, typical for in vivo experiments. We developed a 2PM image simulator to supplement scarce training data. The simulator mimics realistic motion blur, background fluorescence, and shot noise observed in vivo imaging. Training a data-driven model with simulated data improves localisation quality in simulated images and shows why intensity-based methods fail.

AB - Tracking single molecules is instrumental for quantifying the transport of molecules and nanoparticles in biological samples, e.g., in brain drug delivery studies. Existing intensity-based localisation methods are not developed for imaging with a scanning microscope, typically used for in vivo imaging. Low signal-to-noise ratios, movement of molecules out-of-focus, and high motion blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose a challenge to the accurate localisation of molecules. Using data-driven models is challenging due to low data volumes, typical for in vivo experiments. We developed a 2PM image simulator to supplement scarce training data. The simulator mimics realistic motion blur, background fluorescence, and shot noise observed in vivo imaging. Training a data-driven model with simulated data improves localisation quality in simulated images and shows why intensity-based methods fail.

M3 - Preprint

BT - Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images

PB - arXiv.org

ER -

ID: 383101353