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TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography

TractoInferno is the world’s largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 diff...

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Autores principales: Poulin, Philippe, Theaud, Guillaume, Rheault, Francois, St-Onge, Etienne, Bore, Arnaud, Renauld, Emmanuelle, de Beaumont, Louis, Guay, Samuel, Jodoin, Pierre-Marc, Descoteaux, Maxime
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9700736/
https://www.ncbi.nlm.nih.gov/pubmed/36433966
http://dx.doi.org/10.1038/s41597-022-01833-1
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author Poulin, Philippe
Theaud, Guillaume
Rheault, Francois
St-Onge, Etienne
Bore, Arnaud
Renauld, Emmanuelle
de Beaumont, Louis
Guay, Samuel
Jodoin, Pierre-Marc
Descoteaux, Maxime
author_facet Poulin, Philippe
Theaud, Guillaume
Rheault, Francois
St-Onge, Etienne
Bore, Arnaud
Renauld, Emmanuelle
de Beaumont, Louis
Guay, Samuel
Jodoin, Pierre-Marc
Descoteaux, Maxime
author_sort Poulin, Philippe
collection PubMed
description TractoInferno is the world’s largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.
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spelling pubmed-97007362022-11-27 TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography Poulin, Philippe Theaud, Guillaume Rheault, Francois St-Onge, Etienne Bore, Arnaud Renauld, Emmanuelle de Beaumont, Louis Guay, Samuel Jodoin, Pierre-Marc Descoteaux, Maxime Sci Data Data Descriptor TractoInferno is the world’s largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography. Nature Publishing Group UK 2022-11-25 /pmc/articles/PMC9700736/ /pubmed/36433966 http://dx.doi.org/10.1038/s41597-022-01833-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Poulin, Philippe
Theaud, Guillaume
Rheault, Francois
St-Onge, Etienne
Bore, Arnaud
Renauld, Emmanuelle
de Beaumont, Louis
Guay, Samuel
Jodoin, Pierre-Marc
Descoteaux, Maxime
TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography
title TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography
title_full TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography
title_fullStr TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography
title_full_unstemmed TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography
title_short TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography
title_sort tractoinferno - a large-scale, open-source, multi-site database for machine learning dmri tractography
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9700736/
https://www.ncbi.nlm.nih.gov/pubmed/36433966
http://dx.doi.org/10.1038/s41597-022-01833-1
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