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.beSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
In various scientific domains, projects leading to terabytes of multi-gigapixel images become increasingly common (The data deluge, 2012) e.g. biomedical research studies often rely on whole-slide virtual microscopy or automated volume electron microscopy
Significant advances could be made by multidisciplinary collaboration involving distributed groups of life scientists and computer scientists exploiting large-scale image networks (Moody et al, 2013; Poldrack, 2014), or eventually by enlisting the help of members of the general public in large imaging surveys (Clery, 2011) through interactive games (e.g. EyeWire and Brainflight projects)
Developers of image processing algorithms are willing to collaborate with machine learning specialists to build complementary image analysis workflows
Summary
In various scientific domains (incl. biology, biomedicine, astronomy, botany, geology, paleobiology, marine research, aerobiology, climatology), projects leading to terabytes of multi-gigapixel images become increasingly common (The data deluge, 2012) e.g. biomedical research studies often rely on whole-slide virtual microscopy or automated volume electron microscopy. We present Cytomine, a novel open-source, rich web environment to enable highly collaborative analysis of multigigapixel imaging data This tool has been designed with the following objectives in mind:. We want to break common practices in this domain where imaging datasets, quantification results and associated knowledge are still often stored and analyzed within the restricted circle of a specific laboratory To achieve this goal, the Cytomine platform permits active collaboration between distributed groups of life scientists, computer scientists and citizen scientists.
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