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.

Základní údaje

Originální název

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

Autoři

Vydání

Patterns, Cell Press, 2020, 2666-3899

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/20:00115769

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

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

Štítky

cbia-web

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 14. 5. 2021 05:55, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

LTC17016, projekt VaV

Název: Benchmarking algoritmů segmentace a sledování buněk

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Benchmarking algoritmů segmentace a sledování buněk, 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 a Sébastien TOSI. BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows. Patterns. Cell Press, 2020, roč. 1, č. 3, s. 1-10. ISSN 2666-3899. Dostupné z: https://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 = {https://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 a Sébastien TOSI. BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows. \textit{Patterns}. Cell Press, 2020, roč.~1, č.~3, s.~1-10. ISSN~2666-3899. Dostupné z: https://doi.org/10.1016/j.patter.2020.100040.

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