%0 Journal Article %J Sci Rep %D 2018 %T Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach. %A Vandaele, Rémy %A Aceto, Jessica %A Muller, Marc %A Péronnet, Frédérique %A Debat, Vincent %A Wang, Ching-Wei %A Huang, Cheng-Ta %A Jodogne, Sébastien %A Martinive, Philippe %A Geurts, Pierre %A Marée, Raphaël %K Algorithms %K Animals %K Body Weights and Measures %K Drosophila %K Humans %K Image Processing, Computer-Assisted %K Software %K Zebrafish %X

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research.

%B Sci Rep %V 8 %P 538 %8 2018 01 11 %G eng %N 1 %R 10.1038/s41598-017-18993-5 %0 Journal Article %J Bioinformatics %D 2016 %T Collaborative analysis of multi-gigapixel imaging data using Cytomine. %A Marée, Raphaël %A Rollus, Loïc %A Stévens, Benjamin %A Hoyoux, Renaud %A Louppe, Gilles %A Vandaele, Rémy %A Begon, Jean-Michel %A Kainz, Philipp %A Geurts, Pierre %A Wehenkel, Louis %K Image Interpretation, Computer-Assisted %K Internet %K Software %K Statistics as Topic %X

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.

%B Bioinformatics %V 32 %P 1395-401 %8 2016 05 01 %G eng %N 9 %R 10.1093/bioinformatics/btw013