MSBOTS: a multiple small biological organism tracking system robust against non-ideal detection and segmentation conditions

Wang, X; Cheng, E; Burnett, IS

MSBOTS: a multiple small biological organism tracking system robust against non-ideal detection and segmentation conditions

Wang, X Cheng, E

Burnett, IS

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Publisher:: PeerJ Inc.
Publication Type:: Journal Article
Citation:: PEERJ, 2021, 9, pp. 1-20
Issue Date:: 2021-07-27

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Field	Value	Language
dc.contributor.author	Wang, X
dc.contributor.author	Cheng, E https://orcid.org/0000-0003-1632-8062
dc.contributor.author	Burnett, IS
dc.date.accessioned	2023-03-27T10:20:07Z
dc.date.available	2021-06-19
dc.date.available	2023-03-27T10:20:07Z
dc.date.issued	2021-07-27
dc.identifier.citation	PEERJ, 2021, 9, pp. 1-20
dc.identifier.issn	2167-8359
dc.identifier.issn	2167-8359
dc.identifier.uri	http://hdl.handle.net/10453/168612
dc.description.abstract	Accurately tracking a group of small biological organisms using algorithms to obtain their movement trajectories is essential to biomedical and pharmaceutical research. However, object mis-detection, segmentation errors and overlapped individual trajectories are particularly common issues that restrict the development of automatic multiple small organism tracking research. Extending on previous work, this paper presents an accurate and generalised Multiple Small Biological Organism Tracking System (MSBOTS), whose general feasibility is tested on three types of organisms. Evaluated on zebrafish, Artemia and Daphnia video datasets with a wide variety of imaging conditions, the proposed system exhibited decreased overall Multiple Object Tracking Precision (MOTP) errors of up to 77.59%. Moreover, MSBOTS obtained more reliable tracking trajectories with a decreased standard deviation of up to 47.68 pixels compared with the state-of-the-art idTracker system. This paper also presents a behaviour analysis module to study the locomotive characteristics of individual organisms from the obtained tracking trajectories. The developed MSBOTS with the locomotive analysis module and the tested video datasets are made freely available online for public research use.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	PeerJ Inc.
dc.relation.ispartof	PEERJ
dc.relation.isbasedon	10.7717/peerj.11750
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	06 Biological Sciences, 11 Medical and Health Sciences
dc.title	MSBOTS: a multiple small biological organism tracking system robust against non-ideal detection and segmentation conditions
dc.type	Journal Article
utslib.citation.volume	9
utslib.location.activity	United States
utslib.for	06 Biological Sciences
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-27T10:19:57Z
pubs.publication-status	Published
pubs.volume	9

Abstract:

Accurately tracking a group of small biological organisms using algorithms to obtain their movement trajectories is essential to biomedical and pharmaceutical research. However, object mis-detection, segmentation errors and overlapped individual trajectories are particularly common issues that restrict the development of automatic multiple small organism tracking research. Extending on previous work, this paper presents an accurate and generalised Multiple Small Biological Organism Tracking System (MSBOTS), whose general feasibility is tested on three types of organisms. Evaluated on zebrafish, Artemia and Daphnia video datasets with a wide variety of imaging conditions, the proposed system exhibited decreased overall Multiple Object Tracking Precision (MOTP) errors of up to 77.59%. Moreover, MSBOTS obtained more reliable tracking trajectories with a decreased standard deviation of up to 47.68 pixels compared with the state-of-the-art idTracker system. This paper also presents a behaviour analysis module to study the locomotive characteristics of individual organisms from the obtained tracking trajectories. The developed MSBOTS with the locomotive analysis module and the tested video datasets are made freely available online for public research use.

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http://hdl.handle.net/10453/168612