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Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool

Advanced cardiac modeling studies rely on the ability to generate and functionalize personalized in silico models from tomographic multi-label image stacks. Eventually, this is used for building virtual cohorts that capture the variability in size, shape, and morphology of individual hearts. Typical...

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Detalles Bibliográficos
Autores principales: Neic, Aurel, Gsell, Matthias A.F., Karabelas, Elias, Prassl, Anton J., Plank, Gernot
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326605/
https://www.ncbi.nlm.nih.gov/pubmed/32607406
http://dx.doi.org/10.1016/j.softx.2020.100454
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author Neic, Aurel
Gsell, Matthias A.F.
Karabelas, Elias
Prassl, Anton J.
Plank, Gernot
author_facet Neic, Aurel
Gsell, Matthias A.F.
Karabelas, Elias
Prassl, Anton J.
Plank, Gernot
author_sort Neic, Aurel
collection PubMed
description Advanced cardiac modeling studies rely on the ability to generate and functionalize personalized in silico models from tomographic multi-label image stacks. Eventually, this is used for building virtual cohorts that capture the variability in size, shape, and morphology of individual hearts. Typical modeling workflows involve a multitude of interactive mesh manipulation steps, rendering model generation expensive. Meshtool is software specifically designed for automating all complex mesh manipulation tasks emerging in such workflows by implementing algorithms for tasks describable as operations on label fields and/or geometric features. We illustrate how Meshtool increases efficiency and reduces costs by offering an automatable, high performance mesh manipulation toolbox.
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spelling pubmed-73266052020-06-30 Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool Neic, Aurel Gsell, Matthias A.F. Karabelas, Elias Prassl, Anton J. Plank, Gernot SoftwareX Article Advanced cardiac modeling studies rely on the ability to generate and functionalize personalized in silico models from tomographic multi-label image stacks. Eventually, this is used for building virtual cohorts that capture the variability in size, shape, and morphology of individual hearts. Typical modeling workflows involve a multitude of interactive mesh manipulation steps, rendering model generation expensive. Meshtool is software specifically designed for automating all complex mesh manipulation tasks emerging in such workflows by implementing algorithms for tasks describable as operations on label fields and/or geometric features. We illustrate how Meshtool increases efficiency and reduces costs by offering an automatable, high performance mesh manipulation toolbox. 2020-03-20 2020 /pmc/articles/PMC7326605/ /pubmed/32607406 http://dx.doi.org/10.1016/j.softx.2020.100454 Text en http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Neic, Aurel
Gsell, Matthias A.F.
Karabelas, Elias
Prassl, Anton J.
Plank, Gernot
Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool
title Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool
title_full Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool
title_fullStr Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool
title_full_unstemmed Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool
title_short Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool
title_sort automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using meshtool
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326605/
https://www.ncbi.nlm.nih.gov/pubmed/32607406
http://dx.doi.org/10.1016/j.softx.2020.100454
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