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High quality statistical shape modelling of the human nasal cavity and applications.

ABSTRACT: The human nose is a complex organ that shows large morphological variations and has many important functions. However, the relation between shape and function is not yet fully understood. In this work, we present a high quality statistical shape model of the human nose based on clinical CT data of 46 patients. A technique based on cylindrical parametrization was used to create a correspondence between the nasal shapes of the population. Applying principal component analysis on these corresponded nasal cavities resulted in an average nasal geometry and geometrical variations, known as principal components, present in the population with a high precision. The analysis led to 46 principal components, which account for 95% of the total geometrical variation captured. These variations are first discussed qualitatively, and the effect on the average nasal shape of the first five principal components is visualized. Hereafter, by using this statistical shape model, two application examples that lead to quantitative data are shown: nasal shape in function of age and gender, and a morphometric analysis of different anatomical regions. Shape models, as the one presented here, can help to get a better understanding of nasal shape and variation, and their relationship with demographic data.

SUBMITTER: Keustermans W

PROVIDER: S-EPMC6304114 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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High quality statistical shape modelling of the human nasal cavity and applications.

Keustermans William W Huysmans Toon T Danckaers Femke F Zarowski Andrzej A Schmelzer Bert B Sijbers Jan J Dirckx Joris J J JJJ

Royal Society open science 20181219 12

The human nose is a complex organ that shows large morphological variations and has many important functions. However, the relation between shape and function is not yet fully understood. In this work, we present a high quality statistical shape model of the human nose based on clinical CT data of 46 patients. A technique based on cylindrical parametrization was used to create a correspondence between the nasal shapes of the population. Applying principal component analysis on these corresponded ...[more]

PMID: 30662757

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Project description:BackgroundBone shapes strongly influence force and moment predictions of kinematic and musculoskeletal models used in motion analysis. The precise determination of joint reference frames is essential for accurate predictions. Since clinical motion analysis typically does not include medical imaging, from which bone shapes may be obtained, scaling methods using reference subjects to create subject-specific bone geometries are widely used.Research questionThis study investigated if lower limb bone shape predictions from skin-based measurements, utilising an underlying statistical shape model (SSM) that corrects for soft tissue artefacts in digitisation, can be used to improve conventional linear scaling methods of bone geometries.MethodsSSMs created from 35 healthy adult femurs and tibiae/fibulae were used to reconstruct bone shapes by minimising the distance between anatomical landmarks on the models and those digitised in the motion laboratory or on medical images. Soft tissue artefacts were quantified from magnetic resonance images and then used to predict distances between landmarks digitised on the skin surface and bone. Reconstruction results were compared to linearly scaled models by measuring root mean squared distances to segmented surfaces, calculating differences of commonly used anatomical measures and the errors in the prediction of the hip joint centre.ResultsSSM reconstructed surface predictions from varying landmark sets from skin and bone landmarks were more accurate compared to linear scaling methods (2.60-2.95 mm vs. 3.66-3.87 mm median error; p < 0.05). No significant differences were found between SSM reconstructions from bony landmarks and SSM reconstructions from digitised landmarks obtained in the motion lab and therefore reconstructions using skin landmarks are as accurate as reconstructions from landmarks obtained from medical images.SignificanceThese results indicate that SSM reconstructions can be used to increase the accuracy in obtaining bone shapes from surface digitised experimental data acquired in motion lab environments.

Dataset Information

High quality statistical shape modelling of the human nasal cavity and applications.

Publications

High quality statistical shape modelling of the human nasal cavity and applications.

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