Related Experiment Video
Updated: Oct 17, 2025
11:28
3D Orbital Tracking in a Modified Two-photon Microscope: An Application to the Tracking of Intracellular Vesicles
Published on: October 1, 2014
10.4K
A review for cell and particle tracking on microscopy images using algorithms and deep learning technologies.
Hui-Jun Cheng1, Ching-Hsien Hsu2, Che-Lun Hung3
1Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China; Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan.
Biomedical Journal
|October 10, 2021
Summary
This review summarizes time-lapse microscopy analysis for cell tracking and particle tracking. It highlights current methods, identifies challenges, and suggests future research directions for biological applications.
Area of Science:
- Biotechnology
- Cell Biology
- Microscopy
Background:
- Time-lapse microscopy is crucial for observing biological processes like cell motion and survival.
- Accurate tracking of cells and particles is vital for virus research and drug design.
Purpose of the Study:
- To review existing methods for single cell tracking and single particle tracking.
- To identify limitations in current tracking technologies.
- To propose future research avenues in biological imaging analysis.
Main Methods:
- Comprehensive literature review of algorithms and deep learning techniques for biological image analysis.
- Categorization of existing single cell and single particle tracking methods.
- Analysis of research trends and identified challenges.
Main Results:
- A summary of diverse tracking algorithms and deep learning approaches.
- Identification of key challenges in current tracking accuracy and efficiency.
- Outline of emerging research topics and potential innovations.
Conclusions:
- The field of biological tracking has advanced significantly with computational methods.
- Addressing current limitations will drive innovation in microscopy data analysis.
- Future work should focus on developing more robust and automated tracking solutions.
Keywords:
Algorithms and deep learningMicroscopy imagesSegmentationSingle cell trackingSingle particle tracking
