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Improving skeleton algorithm for helping Caenorhabditis elegans trackers

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Improving skeleton algorithm for helping Caenorhabditis elegans trackers

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dc.contributor.author Layana-Castro, Pablo Emmanuel es_ES
dc.contributor.author Puchalt-Rodríguez, Joan Carles es_ES
dc.contributor.author Sánchez Salmerón, Antonio José es_ES
dc.date.accessioned 2021-11-05T14:07:45Z
dc.date.available 2021-11-05T14:07:45Z
dc.date.issued 2020-12-17 es_ES
dc.identifier.issn 2045-2322 es_ES
dc.identifier.uri http://hdl.handle.net/10251/176301
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship This study was supported by the Plan Nacional de I+D with Project RTI2018-094312-B-I00 and by European FEDER funds. ADM Nutrition, Biopolis S.L. and Archer Daniels Midland supplied the C. elegans plates. Some strains were provided by the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). Mrs. Maria-Gabriela Salazar-Secada developed the skeleton annotation application. Mr. Jordi Tortosa-Grau annotated worm skeletons. es_ES
dc.language Inglés es_ES
dc.publisher Nature Publishing Group es_ES
dc.relation.ispartof Scientific Reports es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Image processing es_ES
dc.subject Skeleton algorithm es_ES
dc.subject C. elegans es_ES
dc.subject Lifespan es_ES
dc.subject Healthspan es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Improving skeleton algorithm for helping Caenorhabditis elegans trackers es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1038/s41598-020-79430-8 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094312-B-I00/ES/MONITORIZACION AVANZADA DE COMPORTAMIENTOS DE CAENORHABDITIS ELEGANS, BASADA EN VISION ACTIVA, PARA ANALIZAR FUNCION COGNITIVA Y ENVEJECIMIENTO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//P40 OD010440/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Layana-Castro, PE.; Puchalt-Rodríguez, JC.; Sánchez Salmerón, AJ. (2020). Improving skeleton algorithm for helping Caenorhabditis elegans trackers. Scientific Reports. 10(1):1-12. https://doi.org/10.1038/s41598-020-79430-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1038/s41598-020-79430-8 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 12 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 1 es_ES
dc.identifier.pmid 33335258 es_ES
dc.identifier.pmcid PMC7746747 es_ES
dc.relation.pasarela S\434273 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder National Institutes of Health, EEUU es_ES
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upv.costeAPC 1570 es_ES


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