Finite element modeling of the human kidney for probabilistic occupant models: Statistical shape analysis and mesh morphing

Statistical shape analysis was conducted on 15 pairs (left and right) of human kidneys. It was shown that the left and right kidney were significantly different in size and shape. In addition, several common modes of kidney variation were identified using statistical shape analysis. Semi-automatic m...

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Bibliographic Details
Published in:Journal of biomechanics Vol. 74; pp. 50 - 56
Main Authors: Yates, Keegan M., Untaroiu, Costin D.
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 06.06.2018
Elsevier Limited
Subjects:
Abdomen
Anthropomorphism
Automation
Datasets
Design
Females
Finite Element Analysis
Finite element method
Geometry
Human body
Human kidney
Humans
Injuries
Kidney
Kidneys
Mathematical models
Mean and boundary models
Medical equipment
Mesh morphing
Models, Statistical
Morphing
Posture
Principal component analysis
Principal components analysis
Prostheses and Implants
Statistical analysis
Statistical shape analysis
Studies
Statistical shape analysis
Human kidney
Mesh morphing
Mean and boundary models
Principal component analysis
ISSN:0021-9290, 1873-2380, 1873-2380
Online Access:Get full text
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Summary:Statistical shape analysis was conducted on 15 pairs (left and right) of human kidneys. It was shown that the left and right kidney were significantly different in size and shape. In addition, several common modes of kidney variation were identified using statistical shape analysis. Semi-automatic mesh morphing techniques have been developed to efficiently create subject specific meshes from a template mesh with a similar geometry. Subject specific meshes as well as probabilistic kidney meshes were created from a template mesh. Mesh quality remained about the same as the template mesh while only taking a fraction of the time to create the mesh from scratch or morph with manually identified landmarks. This technique can help enhance the quality of information gathered from experimental testing with subject specific meshes as well as help to more efficiently predict injury by creating models with the mean shape as well as models at the extremes for each principal component.
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ISSN:0021-9290
1873-2380
1873-2380
DOI:10.1016/j.jbiomech.2018.04.016