Deep-learning recognition and tracking of individual nanotubes in low-contrast microscopy videos

Autor:	Pimonov, Vladimir, Tahir, Said, Jourdain, Vincent
Rok vydání:	2024
Předmět:	Condensed Matter - Mesoscale and Nanoscale Physics Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing I.5.4
Druh dokumentu:	Working Paper
Popis:	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. Comment: 13 pages, 5 Figures, No supporting information included
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.13594 Zobrazit plný text záznamu View this record from Arxiv