Résumé
This study addresses the challenge of analyzing the growth kinetics of carbon
nanotubes using in-situ homodyne polarization microscopy (HPM) by developing an
automated deep learning (DL) approach. A Mask-RCNN architecture, enhanced with
a ResNet-50 backbone, was employed to recognize and track individual nanotubes
in microscopy videos, significantly improving the efficiency and
reproducibility of kinetic data extraction. The method involves a series of
video processing steps to enhance contrast and used differential treatment
techniques to manage low signal and fast kinetics. The DL model demonstrates
consistency with manual measurements and increased throughput, laying the
foundation for statistical studies of nanotube growth. The approach can be
adapted for other types of in-situ microscopy studies, emphasizing the
importance of automation in high-throughput data acquisition for research on
individual nano-objects.