@article {1286, title = {Cytomine: Toward an Open and Collaborative Software Platform for Digital Pathology Bridged to Molecular Investigations.}, journal = {Proteomics Clin Appl}, volume = {13}, year = {2019}, month = {2019 01}, pages = {e1800057}, abstract = {

PURPOSE: Digital histology is being increasingly used in research and clinical applications. In parallel, new tissue imaging methods (e.g., imaging mass spectrometry) are currently regarded as very promising approaches for better molecular diagnosis in pathology. However, these new data sources are still often underexploited because of the lack of collaborative software to share and correlate information for multimodal analysis.

EXPERIMENTAL DESIGN: The open science paradigm is followed to develop new features in the web-based Cytomine software to support next-generation digital pathology bridged to molecular investigations.

RESULTS: New open-source developments allow to explore whole-slide classical histology with Matrix Assisted Laser Desorption Ionisation (MALDI) imaging and to support preprocessing for biomarker discovery using laser microdissection-based microproteomics.

CONCLUSIONS AND CLINICAL RELEVANCE: The updated version of Cytomine is the first open and web-based tool to enable sharing data from classical histology, molecular imaging, and cell counting for proteomics preprocessing. It holds good promise to fulfill imminent needs in molecular histopathology.

}, keywords = {Intersectoral Collaboration, Multimodal Imaging, Pathology, Proteomics, Software, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization}, issn = {1862-8354}, doi = {10.1002/prca.201800057}, author = {Rubens, Ulysse and Hoyoux, Renaud and Vanosmael, Laurent and Ouras, Mehdy and Tasset, Maxime and Hamilton, Christopher and Longuesp{\'e}e, R{\'e}mi and Mar{\'e}e, Rapha{\"e}l} } @article {1288, title = {Collaborative analysis of multi-gigapixel imaging data using Cytomine.}, journal = {Bioinformatics}, volume = {32}, year = {2016}, month = {2016 05 01}, pages = {1395-401}, 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.

}, keywords = {Image Interpretation, Computer-Assisted, Internet, Software, Statistics as Topic}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw013}, author = {Mar{\'e}e, Rapha{\"e}l and Rollus, Lo{\"\i}c and St{\'e}vens, Benjamin and Hoyoux, Renaud and Louppe, Gilles and Vandaele, R{\'e}my and Begon, Jean-Michel and Kainz, Philipp and Geurts, Pierre and Wehenkel, Louis} }