Improving skeleton algorithm for helping Caenorhabditis elegans trackers

Abstract One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviou...

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Autores principales:	Pablo E. Layana Castro, Joan Carles Puchalt, Antonio-José Sánchez-Salmerón
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ca02d51b7274697bf7065ac3244e2a6
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spelling	oai:doaj.org-article:7ca02d51b7274697bf7065ac3244e2a62021-12-02T13:58:14ZImproving skeleton algorithm for helping Caenorhabditis elegans trackers10.1038/s41598-020-79430-82045-2322https://doaj.org/article/7ca02d51b7274697bf7065ac3244e2a62020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79430-8https://doaj.org/toc/2045-2322Abstract One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.Pablo E. Layana CastroJoan Carles PuchaltAntonio-José Sánchez-SalmerónNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pablo E. Layana Castro Joan Carles Puchalt Antonio-José Sánchez-Salmerón Improving skeleton algorithm for helping Caenorhabditis elegans trackers
description	Abstract One of the main problems when monitoring Caenorhabditis elegans nematodes (C. elegans) is tracking their poses by automatic computer vision systems. This is a challenge given the marked flexibility that their bodies present and the different poses that can be performed during their behaviour individually, which become even more complicated when worms aggregate with others while moving. This work proposes a simple solution by combining some computer vision techniques to help to determine certain worm poses and to identify each one during aggregation or in coiled shapes. This new method is based on the distance transformation function to obtain better worm skeletons. Experiments were performed with 205 plates, each with 10, 15, 30, 60 or 100 worms, which totals 100,000 worm poses approximately. A comparison of the proposed method was made to a classic skeletonisation method to find that 2196 problematic poses had improved by between 22% and 1% on average in the pose predictions of each worm.
format	article
author	Pablo E. Layana Castro Joan Carles Puchalt Antonio-José Sánchez-Salmerón
author_facet	Pablo E. Layana Castro Joan Carles Puchalt Antonio-José Sánchez-Salmerón
author_sort	Pablo E. Layana Castro
title	Improving skeleton algorithm for helping Caenorhabditis elegans trackers
title_short	Improving skeleton algorithm for helping Caenorhabditis elegans trackers
title_full	Improving skeleton algorithm for helping Caenorhabditis elegans trackers
title_fullStr	Improving skeleton algorithm for helping Caenorhabditis elegans trackers
title_full_unstemmed	Improving skeleton algorithm for helping Caenorhabditis elegans trackers
title_sort	improving skeleton algorithm for helping caenorhabditis elegans trackers
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7ca02d51b7274697bf7065ac3244e2a6
work_keys_str_mv	AT pabloelayanacastro improvingskeletonalgorithmforhelpingcaenorhabditiseleganstrackers AT joancarlespuchalt improvingskeletonalgorithmforhelpingcaenorhabditiseleganstrackers AT antoniojosesanchezsalmeron improvingskeletonalgorithmforhelpingcaenorhabditiseleganstrackers
_version_	1718392208037511168

Improving skeleton algorithm for helping Caenorhabditis elegans trackers

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