Benjamin Stévens

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 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.

A rich internet application for remote visualization and collaborative annotation of digital slides in histology and cytology

Digital slide scanning is advancing the field of pathology and biomedical research, resulting in very large amounts of imaging data. From the computer science point of view, it is challenging to efficiently share, annotate and analyze such data due to their distinct geographical localizations, their high dimensionality, and their numerous sources of variability (scanning equipments, file formats, acquisition protocols, application domains, ...).

A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning

We present a novel methodology combining Web-based software development practices, machine learning, and spatial databases for computer-aided quantification of regions of interest (ROIs) in large-scale imaging data. We describe our main methodological choices, and then illustrate the benefits of the approach (workload reduction, improved precision, scalability, and traceability) on hundreds of whole-slide images of biological tissue slices in cancer research.