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

Rasmus Netterstrøm; Nikolay Kutuzov; Sune Darkner; Maurits Jørring Pallesen; Martin Johannes Lauritzen; Kenny Erleben; Francois Lauze

arXiv · 2303.16903

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

Rasmus Netterstrøm·Nikolay Kutuzov·Sune Darkner·Maurits Jørring Pallesen·Martin Johannes Lauritzen·Kenny Erleben·Francois Lauze

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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.

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q-bio.QM cs.AI cs.CV eess.IV

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BibTeX RIS

Rasmus Netterstrøm, Nikolay Kutuzov, Sune Darkner, Maurits Jørring Pallesen, Martin Johannes Lauritzen, Kenny Erleben, Francois Lauze. 2023-03-17. Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images. https://arxiv.org/abs/2303.16903

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