Cargando…
Separating positional noise from neutral alignment in multicomponent statistical shape models
Given sufficient training samples, statistical shape models can provide detailed population representations for use in anthropological and computational genetic studies, injury biomechanics, musculoskeletal disease models or implant design optimization. While the technique has become extremely popul...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063239/ https://www.ncbi.nlm.nih.gov/pubmed/32181268 http://dx.doi.org/10.1016/j.bonr.2020.100243 |
_version_ | 1783504677439012864 |
---|---|
author | Audenaert, E.A. Van den Eynde, J. de Almeida, D.F. Steenackers, G. Vandermeulen, D. Claes, P. |
author_facet | Audenaert, E.A. Van den Eynde, J. de Almeida, D.F. Steenackers, G. Vandermeulen, D. Claes, P. |
author_sort | Audenaert, E.A. |
collection | PubMed |
description | Given sufficient training samples, statistical shape models can provide detailed population representations for use in anthropological and computational genetic studies, injury biomechanics, musculoskeletal disease models or implant design optimization. While the technique has become extremely popular for the description of isolated anatomical structures, it suffers from positional interference when applied to coupled or articulated input data. In the present manuscript we describe and validate a novel approach to extract positional noise from such coupled data. The technique was first validated and then implemented in a multicomponent model of the lower limb. The impact of noise on the model itself as well as on the description of sexual dimorphism was evaluated. The novelty of our methodology lies in the fact that no rigid transformations are calculated or imposed on the data by means of idealized joint definitions and by extension the models obtained from them. |
format | Online Article Text |
id | pubmed-7063239 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-70632392020-03-16 Separating positional noise from neutral alignment in multicomponent statistical shape models Audenaert, E.A. Van den Eynde, J. de Almeida, D.F. Steenackers, G. Vandermeulen, D. Claes, P. Bone Rep Articles from the Special Issue on Computational Methods in Bone Research; Edited by Dr Penny Atkins and Dr Patrik Christen Given sufficient training samples, statistical shape models can provide detailed population representations for use in anthropological and computational genetic studies, injury biomechanics, musculoskeletal disease models or implant design optimization. While the technique has become extremely popular for the description of isolated anatomical structures, it suffers from positional interference when applied to coupled or articulated input data. In the present manuscript we describe and validate a novel approach to extract positional noise from such coupled data. The technique was first validated and then implemented in a multicomponent model of the lower limb. The impact of noise on the model itself as well as on the description of sexual dimorphism was evaluated. The novelty of our methodology lies in the fact that no rigid transformations are calculated or imposed on the data by means of idealized joint definitions and by extension the models obtained from them. Elsevier 2020-01-11 /pmc/articles/PMC7063239/ /pubmed/32181268 http://dx.doi.org/10.1016/j.bonr.2020.100243 Text en © 2020 The Authors 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 | Articles from the Special Issue on Computational Methods in Bone Research; Edited by Dr Penny Atkins and Dr Patrik Christen Audenaert, E.A. Van den Eynde, J. de Almeida, D.F. Steenackers, G. Vandermeulen, D. Claes, P. Separating positional noise from neutral alignment in multicomponent statistical shape models |
title | Separating positional noise from neutral alignment in multicomponent statistical shape models |
title_full | Separating positional noise from neutral alignment in multicomponent statistical shape models |
title_fullStr | Separating positional noise from neutral alignment in multicomponent statistical shape models |
title_full_unstemmed | Separating positional noise from neutral alignment in multicomponent statistical shape models |
title_short | Separating positional noise from neutral alignment in multicomponent statistical shape models |
title_sort | separating positional noise from neutral alignment in multicomponent statistical shape models |
topic | Articles from the Special Issue on Computational Methods in Bone Research; Edited by Dr Penny Atkins and Dr Patrik Christen |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063239/ https://www.ncbi.nlm.nih.gov/pubmed/32181268 http://dx.doi.org/10.1016/j.bonr.2020.100243 |
work_keys_str_mv | AT audenaertea separatingpositionalnoisefromneutralalignmentinmulticomponentstatisticalshapemodels AT vandeneyndej separatingpositionalnoisefromneutralalignmentinmulticomponentstatisticalshapemodels AT dealmeidadf separatingpositionalnoisefromneutralalignmentinmulticomponentstatisticalshapemodels AT steenackersg separatingpositionalnoisefromneutralalignmentinmulticomponentstatisticalshapemodels AT vandermeulend separatingpositionalnoisefromneutralalignmentinmulticomponentstatisticalshapemodels AT claesp separatingpositionalnoisefromneutralalignmentinmulticomponentstatisticalshapemodels |