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

J 2020

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

RUBENS, Ulysse, Romain MORMONT, Lassi PAAVOLAINEN, Volker BÄCKER, Benjamin PAVIE et. al.

Basic information

Original name

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

Authors

RUBENS, Ulysse (56 Belgium), Romain MORMONT (56 Belgium), Lassi PAAVOLAINEN (246 Finland), Volker BÄCKER (250 France), Benjamin PAVIE (56 Belgium), Leandro A. SCHOLZ (76 Brazil), Gino MICHIELS (56 Belgium), Martin MAŠKA (203 Czech Republic, guarantor, belonging to the institution), Devrim ÜNAY (792 Turkey), Graeme BALL (826 United Kingdom of Great Britain and Northern Ireland), Renaud HOYOUX (826 United Kingdom of Great Britain and Northern Ireland), Rémy VANDAELE (56 Belgium), Ofra GOLANI (376 Israel), Stefan G. STANCIU (642 Romania), Natasa SLADOJE (752 Sweden), Perrine PAUL-GILLOTEAUX (250 France), Raphaël MARÉE (56 Belgium) and Sébastien TOSI (724 Spain)

Edition

Patterns, Cell Press, 2020, 2666-3899

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

RIV identification code

RIV/00216224:14330/20:00115769

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1016/j.patter.2020.100040

UT WoS

000653824900007

Keywords in English

image analysis; software benchmarking; deployment; reproducibility; bioimaging; deep learning

Abstract

V originále

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.

Links

LTC17016, research and development project

Name: Benchmarking algoritmů segmentace a sledování buněk

Investor: Ministry of Education, Youth and Sports of the CR, Benchmarking of algorithms for cell segmentation and tracking, INTER-COST

Citovat

RUBENS, Ulysse, Romain MORMONT, Lassi PAAVOLAINEN, Volker BÄCKER, Benjamin PAVIE, Leandro A. SCHOLZ, Gino MICHIELS, Martin MAŠKA, Devrim ÜNAY, Graeme BALL, Renaud HOYOUX, Rémy VANDAELE, Ofra GOLANI, Stefan G. STANCIU, Natasa SLADOJE, Perrine PAUL-GILLOTEAUX, Raphaël MARÉE and Sébastien TOSI. BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows. Patterns. Cell Press, 2020, vol. 1, No 3, p. 1-10. ISSN 2666-3899. Available from: https://dx.doi.org/10.1016/j.patter.2020.100040.

@article{1661361,
   author = {Rubens, Ulysse and Mormont, Romain and Paavolainen, Lassi and Bäcker, Volker and Pavie, Benjamin and Scholz, Leandro A. and Michiels, Gino and Maška, Martin and Ünay, Devrim and Ball, Graeme and Hoyoux, Renaud and Vandaele, Rémy and Golani, Ofra and Stanciu, Stefan G. and Sladoje, Natasa and PaulandGilloteaux, Perrine and Marée, Raphaël and Tosi, Sébastien},
   article_number = {3},
   doi = {http://dx.doi.org/10.1016/j.patter.2020.100040},
   keywords = {image analysis; software benchmarking; deployment; reproducibility; bioimaging; deep learning},
   language = {eng},
   issn = {2666-3899},
   journal = {Patterns},
   title = {BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows},
   url = {http://dx.doi.org/10.1016/j.patter.2020.100040},
   volume = {1},
   year = {2020}
}

TY  - JOUR
ID  - 1661361
AU  - 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
PY  - 2020
TI  - BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows
JF  - Patterns
VL  - 1
IS  - 3
SP  - 1-10
EP  - 1-10
PB  - Cell Press
SN  - 26663899
KW  - image analysis
KW  - software benchmarking
KW  - deployment
KW  - reproducibility
KW  - bioimaging
KW  - deep learning
UR  - http://dx.doi.org/10.1016/j.patter.2020.100040
N2  - 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.
ER  -

RUBENS, Ulysse, Romain MORMONT, Lassi PAAVOLAINEN, Volker BÄCKER, Benjamin PAVIE, Leandro A. SCHOLZ, Gino MICHIELS, Martin MAŠKA, Devrim ÜNAY, Graeme BALL, Renaud HOYOUX, Rémy VANDAELE, Ofra GOLANI, Stefan G. STANCIU, Natasa SLADOJE, Perrine PAUL-GILLOTEAUX, Rapha$\backslash$''el MARÉE and Sébastien TOSI. BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows. \textit{Patterns}. Cell Press, 2020, vol.~1, No~3, p.~1-10. ISSN~2666-3899. Available from: https://dx.doi.org/10.1016/j.patter.2020.100040.

Detailed Information on Publication Record