用深度学习自动识别追踪低对比度显微视频中的碳纳米管
Deep-learning recognition and tracking of individual nanotubes in low-contrast microscopy videos
- 基于改进的Mask-RCNN模型，结合ResNet-50骨干网络
- 识别准确率与人工标注一致，处理速度提升数倍
- 适合需要高通量分析纳米结构生长动力学的研究者
本研究针对原位同调偏振显微镜（HPM）中碳纳米管生长动力学分析的挑战，提出一种自动化深度学习方法。采用带有ResNet-50主干网络的Mask-RCNN架构，在一系列视频增强和差分处理技术支持下，实现了对低信噪比、快速动态纳米管的精准识别与跟踪。该方法在保持与人工测量一致性的同时，显著提升数据提取效率与可重复性，为纳米管生长的统计研究奠定基础。该框架可推广至其他原位显微研究，凸显自动化在个体纳米对象高通量数据采集中的重要性。
原文摘要 · Abstract (English)
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.
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