A review for cell and particle tracking on microscopy images using algorithms and deep learning technologies

Hui-Jun Cheng; Ching-Hsien Hsu; Che-Lun Hung; Chun-Yuan Lin

doi:10.1016/j.bj.2021.10.001

A review for cell and particle tracking on microscopy images using algorithms and deep learning technologies

Biomed J. 2022 Jun;45(3):465-471. doi: 10.1016/j.bj.2021.10.001. Epub 2021 Oct 7.

Authors

Hui-Jun Cheng¹, Ching-Hsien Hsu², Che-Lun Hung³, Chun-Yuan Lin⁴

Affiliations

¹ Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China; Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan.
² Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan; Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, School of Mathematics and Big Data, Foshan University, Foshan, China; Department of Medical Research, China Medical University Hospital, China Medical University, Taiwan.
³ Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Computer Science and Communication Engineering, Providence University, Taichung, Taiwan.
⁴ Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan; Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan. Electronic address: cyulin@asia.edu.tw.

Abstract

Time-lapse microscopy images generated by biological experiments have been widely used for observing target activities, such as the motion trajectories and survival states. Based on these observations, biologists can conclude experimental results or present new hypotheses for several biological applications, i.e. virus research or drug design. Many methods or tools have been proposed in the past to observe cell and particle activities, which are defined as single cell tracking and single particle tracking problems, by using algorithms and deep learning technologies. In this article, a review for these works is presented in order to summarize the past methods and research topics at first, then points out the problems raised by these works, and finally proposes future research directions. The contributions of this article will help researchers to understand past development trends and further propose innovative technologies.

Keywords: Algorithms and deep learning; Microscopy images; Segmentation; Single cell tracking; Single particle tracking.

MeSH terms

Algorithms
Deep Learning*
Humans
Microscopy* / methods