Separating positional noise from neutral alignment in multicomponent statistical shape models.
Anatomy
Geometric morphometrics
Image analysis
Multivariate regression
Sex dimorphism
Journal
Bone reports
ISSN: 2352-1872
Titre abrégé: Bone Rep
Pays: United States
ID NLM: 101646176
Informations de publication
Date de publication:
Jun 2020
Jun 2020
Historique:
received:
23
10
2019
revised:
26
12
2019
accepted:
06
01
2020
entrez:
18
3
2020
pubmed:
18
3
2020
medline:
18
3
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32181268
doi: 10.1016/j.bonr.2020.100243
pii: S2352-1872(20)30002-4
pii: 100243
pmc: PMC7063239
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100243Informations de copyright
© 2020 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests that could inappropriately influence this work.
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