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Collaborative analysis of multi-gigapixel imaging data using Cytomine

2016; Oxford University Press; Volume: 32; Issue: 9 Linguagem: Inglês

10.1093/bioinformatics/btw013

1367-4811

Raphaël Marée, Loïc Rollus, Benjamin H. Stevens, Renaud Hoyoux, Gilles Louppe, Rémy Vandaele, Jean-Michel Begon, Philipp Kainz, Pierre Geurts, Louis Wehenkel,

Gene expression and cancer classification

Abstract 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.