BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows
| Autor: | Volker Baecker, Renaud Hoyoux, Romain Mormont, Stefan G. Stanciu, Leandro A. Scholz, Graeme Ball, Devrim Unay, Rémy Vandaele, Martin Maška, Perrine Paul-Gilloteaux, Lassi Paavolainen, Ulysse Rubens, Raphaël Marée, Gino Michiels, Benjamin Pavie, Ofra Golani, Natasa Sladoje, Sébastien Tosi |
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| Přispěvatelé: | BioCampus (BCM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Masaryk University [Brno] (MUNI), Biomedical Engineering [Istanbul], Bahcesehir University [Istanbul], Institut Montefiore - Département d'Electricité, Electronique et Informatique (Liège), Structure fédérative de recherche François Bonamy (SFR François Bonamy), Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Santé de l'Université de Nantes (IRS-UN), Department of Electrical Engineering and Computer Science (Institut Montefiore), Université de Liège, ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), European Project: CA15124,NEUBIAS, BioCampus Montpellier (BCM), Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institute for Molecular Medicine Finland, Helsinki Institute of Life Science HiLIFE |
| Rok vydání: | 2020 |
| Předmět: |
Technology
IMAGE Computer science SEGMENTATION 02 engineering and technology Computer Science Artificial Intelligence Software benchmarking bioimaging ComputingMilieux_MISCELLANEOUS 0303 health sciences Computer Science Information Systems Medicinsk bildbehandling Benchmarking web application lcsh:QA76.75-76.765 [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Benchmark (computing) community Computer Science Interdisciplinary Applications [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] Software ecosystem education 0206 medical engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION General Decision Sciences 03 medical and health sciences image analysis RESOURCE deployment Web application reproducibility 030304 developmental biology lcsh:Computer software [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] Science & Technology software business.industry Deep learning deep learning PLATFORM Descriptor 113 Computer and information sciences Data science Medical Image Processing Workflow Computer Science Key (cryptography) Artificial intelligence business 020602 bioinformatics |
| Zdroj: | Patterns, Vol 1, Iss 3, Pp 100040-(2020) Patterns Patterns, 2020, 1, pp.100040. ⟨10.1016/j.patter.2020.100040⟩ Patterns, Cell Press Elsevier, 2020, 1, pp.100040. ⟨10.1016/j.patter.2020.100040⟩ |
| ISSN: | 2666-3899 |
| Popis: | Summary 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. Highlights • Image analysis is inescapable in extracting quantitative data from scientific images • It can be difficult to deploy and apply state-of-the-art image analysis methods • Comparing heterogeneous image analysis methods is tedious and error prone • We introduce a platform to deploy and fairly compare image analysis workflows The Bigger Picture Image analysis is currently one of the major hurdles in the bioimaging chain, especially for large datasets. BIAFLOWS seeds the ground for virtual access to image analysis workflows running in high-performance computing environments. Providing a broader access to state-of-the-art image analysis is expected to have a strong impact on research in biology, and in other fields where image analysis is a critical step in extracting scientific results from images. BIAFLOWS could also be adopted as a federated platform to publish microscopy images together with the workflows that were used to extract scientific data from these images. This is a milestone of open science that will help to accelerate scientific progress by fostering collaborative practices. While image analysis is becoming inescapable in the extraction of quantitative information from scientific images, it is currently challenging for life scientists to find, test, and compare state-of-the-art image analysis methods compatible with their own microscopy images. It is also difficult and time consuming for algorithm developers to validate and reproducibly share their methods. BIAFLOWS is a web platform addressing these needs. It can be used as a local solution or through an immediately accessible and curated online instance. |
| Databáze: | OpenAIRE |
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