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Adaptive constrained constructive optimisation for complex vascularisation processes
Mimicking angiogenetic processes in vascular territories acquires importance in the analysis of the multi-scale circulatory cascade and the coupling between blood flow and cell function. The present work extends, in several aspects, the Constrained Constructive Optimisation (CCO) algorithm to tackle...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969782/ https://www.ncbi.nlm.nih.gov/pubmed/33731776 http://dx.doi.org/10.1038/s41598-021-85434-9 |
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author | Talou, Gonzalo Daniel Maso Safaei, Soroush Hunter, Peter John Blanco, Pablo Javier |
author_facet | Talou, Gonzalo Daniel Maso Safaei, Soroush Hunter, Peter John Blanco, Pablo Javier |
author_sort | Talou, Gonzalo Daniel Maso |
collection | PubMed |
description | Mimicking angiogenetic processes in vascular territories acquires importance in the analysis of the multi-scale circulatory cascade and the coupling between blood flow and cell function. The present work extends, in several aspects, the Constrained Constructive Optimisation (CCO) algorithm to tackle complex automatic vascularisation tasks. The main extensions are based on the integration of adaptive optimisation criteria and multi-staged space-filling strategies which enhance the modelling capabilities of CCO for specific vascular architectures. Moreover, this vascular outgrowth can be performed either from scratch or from an existing network of vessels. Hence, the vascular territory is defined as a partition of vascular, avascular and carriage domains (the last one contains vessels but not terminals) allowing one to model complex vascular domains. In turn, the multi-staged space-filling approach allows one to delineate a sequence of biologically-inspired stages during the vascularisation process by exploiting different constraints, optimisation strategies and domain partitions stage by stage, improving the consistency with the architectural hierarchy observed in anatomical structures. With these features, the aDaptive CCO (DCCO) algorithm proposed here aims at improving the modelled network anatomy. The capabilities of the DCCO algorithm are assessed with a number of anatomically realistic scenarios. |
format | Online Article Text |
id | pubmed-7969782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79697822021-03-19 Adaptive constrained constructive optimisation for complex vascularisation processes Talou, Gonzalo Daniel Maso Safaei, Soroush Hunter, Peter John Blanco, Pablo Javier Sci Rep Article Mimicking angiogenetic processes in vascular territories acquires importance in the analysis of the multi-scale circulatory cascade and the coupling between blood flow and cell function. The present work extends, in several aspects, the Constrained Constructive Optimisation (CCO) algorithm to tackle complex automatic vascularisation tasks. The main extensions are based on the integration of adaptive optimisation criteria and multi-staged space-filling strategies which enhance the modelling capabilities of CCO for specific vascular architectures. Moreover, this vascular outgrowth can be performed either from scratch or from an existing network of vessels. Hence, the vascular territory is defined as a partition of vascular, avascular and carriage domains (the last one contains vessels but not terminals) allowing one to model complex vascular domains. In turn, the multi-staged space-filling approach allows one to delineate a sequence of biologically-inspired stages during the vascularisation process by exploiting different constraints, optimisation strategies and domain partitions stage by stage, improving the consistency with the architectural hierarchy observed in anatomical structures. With these features, the aDaptive CCO (DCCO) algorithm proposed here aims at improving the modelled network anatomy. The capabilities of the DCCO algorithm are assessed with a number of anatomically realistic scenarios. Nature Publishing Group UK 2021-03-17 /pmc/articles/PMC7969782/ /pubmed/33731776 http://dx.doi.org/10.1038/s41598-021-85434-9 Text en © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Talou, Gonzalo Daniel Maso Safaei, Soroush Hunter, Peter John Blanco, Pablo Javier Adaptive constrained constructive optimisation for complex vascularisation processes |
title | Adaptive constrained constructive optimisation for complex vascularisation processes |
title_full | Adaptive constrained constructive optimisation for complex vascularisation processes |
title_fullStr | Adaptive constrained constructive optimisation for complex vascularisation processes |
title_full_unstemmed | Adaptive constrained constructive optimisation for complex vascularisation processes |
title_short | Adaptive constrained constructive optimisation for complex vascularisation processes |
title_sort | adaptive constrained constructive optimisation for complex vascularisation processes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969782/ https://www.ncbi.nlm.nih.gov/pubmed/33731776 http://dx.doi.org/10.1038/s41598-021-85434-9 |
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