Publication:
ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging

dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Procesado Multimediaes
dc.contributor.authorMolina Moreno, Miguel
dc.contributor.authorGonzález Díaz, Iván
dc.contributor.authorSicilia, Jon
dc.contributor.authorCrainiciuc, Georgiana
dc.contributor.authorPalomino Segura, Miguel
dc.contributor.authorHidalgo, Andrés
dc.contributor.authorDíaz de María, Fernando
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es
dc.contributor.funderUniversidad Carlos III de Madrides
dc.date.accessioned2023-03-23T17:12:33Z
dc.date.available2023-03-23T17:12:33Z
dc.date.issued2022-04-01
dc.description.abstractCell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by microscopy imaging, although based on automatic frameworks, still require manual supervision at some points of the system. Hence, when dealing with a large amount of data, the analysis becomes incredibly time-consuming and typically yields poor biological information. In this paper, we propose a fully-automated system for segmentation, tracking and feature extraction of migrating cells within blood vessels in 4D microscopy imaging. Our system consists of a robust 3D convolutional neural network (CNN) for joint blood vessel and cell segmentation, a 3D tracking module with collision handling, and a novel method for feature extraction, which takes into account the particular geometry in the cell-vessel arrangement. Experiments on a large 4D intravital microscopy dataset show that the proposed system achieves a significantly better performance than the state-of-the-art tools for cell segmentation and tracking. Furthermore, we have designed an analytical method of cell behaviors based on the automatically extracted features, which supports the hypotheses related to leukocyte migration posed by expert biologists. This is the first time that such a comprehensive automatic analysis of immune cell migration has been performed, where the total population under study reaches hundreds of neutrophils and thousands of time instances.en
dc.description.sponsorshipThis work has been partially supported by the National Grant TEC2017-84395-P of the Spanish Ministry of Economy and Competitiveness, Madrid Regional Government and Universidad Carlos III de Madrid through the project SHARON-CM-UC3M, RTI2018-095497-B-I00 from Ministerio de Ciencia e Innovación (MICINN) and HR17_00527 from Fundación La Caixa to A.H. M.M-M. is supported by the Spanish Ministry of Education, Culture and Sports FPU Grant FPU18/02825. M.P-S. is supported by a Federation of European Biochemical Societies long-term fellowship. J.S. is supported by a fellowship (PRE2019-089130) from MICINN.en
dc.description.statusPublicadoes
dc.format.extent19
dc.identifier.bibliographicCitationMedical Image Analysis, (2022), 77, 102358.en
dc.identifier.doihttps://doi.org/10.1016/j.media.2022.102358
dc.identifier.issn1361-8415
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue102358
dc.identifier.publicationlastpage19
dc.identifier.publicationtitleMEDICAL IMAGE ANALYSISen
dc.identifier.publicationvolume77
dc.identifier.urihttps://hdl.handle.net/10016/36946
dc.identifier.uxxiAR/0000031277
dc.language.isoengen
dc.publisherElsevier
dc.relation.projectIDGobierno de España. TEC2017-84395-Pes
dc.relation.projectIDGobierno de España. RTI2018-095497-B-I00es
dc.relation.projectIDGobierno de España. FPU18/02825es
dc.relation.projectIDGobierno de España. PRE2019-089130es
dc.relation.projectIDAT-2022
dc.relation.projectIDAT-22
dc.rights© 2022 The Authors. Published by Elsevier B.V.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.otherCell trackingen
dc.subject.other3D segmentationen
dc.subject.otherConvolutional neural networksen
dc.subject.otherCNNen
dc.subject.otherCell migrationen
dc.titleACME: Automatic feature extraction for cell migration examination through intravital microscopy imagingen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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