Improved particle filter algorithm combined with culture algorithm for collision Caenorhabditis elegans tracking.

Taoyuan Yu, Xiping Xu, Yuanpeng Li, Ning Zhang, Naiyu Zhang, Xiaohui Wang

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Taoyuan YuSchool of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, China.

Xiping XuSchool of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, China. xxp@cust.edu.cn.

Yuanpeng LiLaboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, Jilin, China.

Ning ZhangSchool of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, China.

Naiyu ZhangSchool of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, China.

Xiaohui WangLaboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, Jilin, China.

Funding

Department of Science and Technology of Jilin Province, China 20210201029GX

6 · The paper itself

Abstract

In order to address the issue of tracking errors of collision Caenorhabditis elegans, this research proposes an improved particle filter tracking method integrated with cultural algorithm. The particle filter algorithm is enhanced through the integration of the sine cosine algorithm, thereby facilitating uninterrupted tracking of the target C. elegans. Furthermore, the cultural algorithm is employed to facilitate recognition of the target C. elegans following a collision. In addition, this method integrates the concepts of down-sample and marking to reduce the average processing time of the image. Ultimately, the experiment was conducted on two strains of C. elegans of six ages. The experimental results demonstrate that the proposed method can accurately identify the target worm in the post-collision stage. The proposed method has the potential to be utilized in the field of worm tracking, offering a novel method into the acquisition of collision C. elegans behavior.

Indexed as

AlgorithmsCaenorhabditis elegansImage Processing, Computer-AssistedAnimalsBehavior, AnimalCollision C. Elegans trackingCultural algorithmParticle filterSine cosine algorithmVisual object tracking

Identifiers

PMID39863688

DOI10.1038/s41598-025-87970-0

PMCPMC11762314

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.