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Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification

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Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification

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dc.contributor.author García-Garví, Antonio es_ES
dc.contributor.author Puchalt-Rodríguez, Joan Carles es_ES
dc.contributor.author Layana-Castro, Pablo Emmanuel es_ES
dc.contributor.author Navarro Moya, Francisco es_ES
dc.contributor.author Sánchez Salmerón, Antonio José es_ES
dc.date.accessioned 2022-10-10T18:08:08Z
dc.date.available 2022-10-10T18:08:08Z
dc.date.issued 2021-07 es_ES
dc.identifier.uri http://hdl.handle.net/10251/187404
dc.description.abstract [EN] The automation of lifespan assays with C. elegans in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining whether a worm is alive or dead can be complex as they barely move during the last few days of their lives. This paper proposes a method combining traditional computer vision techniques with a live/dead C. elegans classifier based on convolutional and recurrent neural networks from low-resolution image sequences. In addition to proposing a new method to automate lifespan, the use of data augmentation techniques is proposed to train the network in the absence of large numbers of samples. The proposed method achieved small error rates (3.54% +/- 1.30% per plate) with respect to the manual curve, demonstrating its feasibility. es_ES
dc.description.sponsorship This study was supported by the Plan Nacional de I + D under the project RTI2018-094312B-I00 and by the 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 es_ES
dc.subject Computer vision es_ES
dc.subject Deep learning es_ES
dc.subject Lifespan automation es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s21144943 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RTI2018-094312-B-I00-AR//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 García-Garví, A.; Puchalt-Rodríguez, JC.; Layana-Castro, PE.; Navarro Moya, F.; Sánchez Salmerón, AJ. (2021). Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification. Sensors. 21(14):1-17. https://doi.org/10.3390/s21144943 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s21144943 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 21 es_ES
dc.description.issue 14 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 34300683 es_ES
dc.identifier.pmcid PMC8309694 es_ES
dc.relation.pasarela S\443493 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 1172,23 es_ES


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