Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images. (arXiv:2303.16903v1 [q-bio.QM])

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.