Paper ID: 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

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.

Submitted: Mar 17, 2023