Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images

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dc.citation.articleNumber797555
dc.citation.journalTitleFrontiers in Bioengineering and Biotechnology
dc.citation.volumeNumber9
dc.contributor.authorSarti, Mattia
dc.contributor.authorParlani, Maria
dc.contributor.authorDiaz-Gomez, Luis
dc.contributor.authorMikos, Antonios G.
dc.contributor.authorCerveri, Pietro
dc.contributor.authorCasarin, Stefano
dc.contributor.authorDondossola, Eleonora
dc.date.accessioned2022-03-24T13:31:39Z
dc.date.available2022-03-24T13:31:39Z
dc.date.issued2022
dc.description.abstractThe Foreign body response (FBR) is a major unresolved challenge that compromises medical implant integration and function by inflammation and fibrotic encapsulation. Mice implanted with polymeric scaffolds coupled to intravital non-linear multiphoton microscopy acquisition enable multiparametric, longitudinal investigation of the FBR evolution and interference strategies. However, follow-up analyses based on visual localization and manual segmentation are extremely time-consuming, subject to human error, and do not allow for automated parameter extraction. We developed an integrated computational pipeline based on an innovative and versatile variant of the U-Net neural network to segment and quantify cellular and extracellular structures of interest, which is maintained across different objectives without impairing accuracy. This software for automatically detecting the elements of the FBR shows promise to unravel the complexity of this pathophysiological process.
dc.identifier.citationSarti, Mattia, Parlani, Maria, Diaz-Gomez, Luis, et al.. "Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images." <i>Frontiers in Bioengineering and Biotechnology,</i> 9, (2022) Frontiers Media S.A.: https://doi.org/10.3389/fbioe.2021.797555.
dc.identifier.digitalfbioe-09-797555
dc.identifier.doihttps://doi.org/10.3389/fbioe.2021.797555
dc.identifier.urihttps://hdl.handle.net/1911/112040
dc.language.isoeng
dc.publisherFrontiers Media S.A.
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDeep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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