The
unique capability of surface plasmon resonance microscopy (SPRM) in single nanoparticle analysis has
found use in various chemical and biological applications. While SPRM
offers exceptional sensitivity, the statistical analysis of numerous
nanoparticles has been extremely laborious and time-consuming. Herein,
we presented an image processing software package for nanoparticle
analysis in SPRM, which is empowered by a deep learning algorithm.
This package enabled fully automated nanoparticle identification,
digital counting, three-dimensional tracking of particle locations,
and quantification of dwell time and Brownian motion properties. With
a built-in image filtering process to improve the contrast, robust
identification and analysis have been achieved from SPRM images of
low refractive index nanoparticles. This software tool would largely
promote the translation of SPRM technology into the digital sensing
platform for high throughput sample screening.