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Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm

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Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm

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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 García-Garví, Antonio es_ES
dc.contributor.author Sánchez Salmerón, Antonio José es_ES
dc.date.accessioned 2022-10-06T18:06:00Z
dc.date.available 2022-10-06T18:06:00Z
dc.date.issued 2021-08-20 es_ES
dc.identifier.uri http://hdl.handle.net/10251/187206
dc.description.abstract [EN] Automatic tracking of Caenorhabditis elegans (C. egans) in standard Petri dishes is challenging due to high-resolution image requirements when fully monitoring a Petri dish, but mainly due to potential losses of individual worm identity caused by aggregation of worms, overlaps and body contact. To date, trackers only automate tests for individual worm behaviors, canceling data when body contact occurs. However, essays automating contact behaviors still require solutions to this problem. In this work, we propose a solution to this difficulty using computer vision techniques. On the one hand, a skeletonization method is applied to extract skeletons in overlap and contact situations. On the other hand, new optimization methods are proposed to solve the identity problem during these situations. Experiments were performed with 70 tracks and 3779 poses (skeletons) of C. elegans. Several cost functions with different criteria have been evaluated, and the best results gave an accuracy of 99.42% in overlapping with other worms and noise on the plate using the modified skeleton algorithm and 98.73% precision using the classical skeleton algorithm es_ES
dc.description.sponsorship This study was supported by the Plan Nacional de I+D with Project RTI2018-094312-B-I00, FPI Predoctoral contract PRE2019-088214 and by European FEDER funds. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject C.elegans assays es_ES
dc.subject Lifespan es_ES
dc.subject Healthspan es_ES
dc.subject Image detection es_ES
dc.subject Multi-tracker es_ES
dc.subject Standard Petri dishes es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s21165622 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.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.; García-Garví, A.; Sánchez Salmerón, AJ. (2021). Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm. Sensors. 21(16):1-21. https://doi.org/10.3390/s21165622 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s21165622 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 21 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 21 es_ES
dc.description.issue 16 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 34451062 es_ES
dc.identifier.pmcid PMC8402443 es_ES
dc.relation.pasarela S\447825 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES
upv.costeAPC 1785,39 es_ES


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