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Collaborative analysis of multi-gigapixel imaging data using Cytomine

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machin...

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Detalles Bibliográficos
Autores principales: Marée, Raphaël, Rollus, Loïc, Stévens, Benjamin, Hoyoux, Renaud, Louppe, Gilles, Vandaele, Rémy, Begon, Jean-Michel, Kainz, Philipp, Geurts, Pierre, Wehenkel, Louis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848407/
https://www.ncbi.nlm.nih.gov/pubmed/26755625
http://dx.doi.org/10.1093/bioinformatics/btw013
Descripción
Sumario:Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications. Availability and implementation: Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/. A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available. Contact: info@cytomine.be Supplementary information: Supplementary data are available at Bioinformatics online.