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

• URL: http://arxiv.org/abs/2303.16903v1
• Date: Fri, 17 Mar 2023 14:48:50 GMT
• Title: Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images
• Authors: Rasmus Netterstr{\o}m, Nikolay Kutuzov, Sune Darkner, Maurits J{\o}rring Pallesen, Martin Johannes Lauritzen, Kenny Erleben, Francois Lauze
• Abstract summary: Existing intensity-based localisation methods are not developed for imaging with a scanning microscope. 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. We developed a 2PM image simulator to supplement scarce training data.
• Score: 6.251942628138834
• License: http://creativecommons.org/licenses/by/4.0/
• 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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