Informace o publikaci

BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows

Autoři	RUBENS Ulysse MORMONT Romain PAAVOLAINEN Lassi BÄCKER Volker PAVIE Benjamin SCHOLZ Leandro A. MICHIELS Gino MAŠKA Martin ÜNAY Devrim BALL Graeme HOYOUX Renaud VANDAELE Rémy GOLANI Ofra STANCIU Stefan G. SLADOJE Natasa PAUL-GILLOTEAUX Perrine MARÉE Raphaël TOSI Sébastien
Rok publikování	2020
Druh	Článek v odborném periodiku
Časopis / Zdroj	Patterns
Fakulta / Pracoviště MU	Fakulta informatiky
Citace
www	http://dx.doi.org/10.1016/j.patter.2020.100040
Doi	http://dx.doi.org/10.1016/j.patter.2020.100040
Klíčová slova	image analysis; software benchmarking; deployment; reproducibility; bioimaging; deep learning
Popis	Image analysis is key to extracting quantitative information from scientific microscopy images, but the methods involved are now often so refined that they can no longer be unambiguously described by written protocols. We introduce BIAFLOWS, an open-source web tool enabling to reproducibly deploy and benchmark bioimage analysis workflows coming from any software ecosystem. A curated instance of BIAFLOWS populated with 34 image analysis workflows and 15 microscopy image datasets recapitulating common bioimage analysis problems is available online. The workflows can be launched and assessed remotely by comparing their performance visually and according to standard benchmark metrics. We illustrated these features by comparing seven nuclei segmentation workflows, including deep-learning methods. BIAFLOWS enables to benchmark and share bioimage analysis workflows, hence safeguarding research results and promoting high-quality standards in image analysis. The platform is thoroughly documented and ready to gather annotated microscopy datasets and workflows contributed by the bioimaging community.
Související projekty:	Benchmarking algoritmů segmentace a sledování buněk