The 3D skull 0–4 years: A validated, generative, statistical shape model

Background: This study aims to capture the 3D shape of the human skull in a healthy paediatric population (0–4 years old) and construct a generative statistical shape model. Methods: The skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (C...

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Autores principales:	Eimear O' Sullivan, Lara S. van de Lande, Anne-Jet C. Oosting, Athanasios Papaioannou, N. Owase Jeelani, Maarten J. Koudstaal, Roman H. Khonsari, David J. Dunaway, Stefanos Zafeiriou, Silvia Schievano
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Paediatric skull Morphometrics Statistical shape model 3D morphable model Synthetic shapes Diseases of the musculoskeletal system RC925-935
Acceso en línea:	https://doaj.org/article/e841774f1aa9415d84d62e1071bda290
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spelling	oai:doaj.org-article:e841774f1aa9415d84d62e1071bda2902021-12-04T04:34:35ZThe 3D skull 0–4 years: A validated, generative, statistical shape model2352-187210.1016/j.bonr.2021.101154https://doaj.org/article/e841774f1aa9415d84d62e1071bda2902021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352187221004113https://doaj.org/toc/2352-1872Background: This study aims to capture the 3D shape of the human skull in a healthy paediatric population (0–4 years old) and construct a generative statistical shape model. Methods: The skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population. Results: The 3DMM of the paediatric skull 0–4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases. Conclusion: This study presents a reliable statistical shape model of the paediatric skull 0–4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field.Eimear O' SullivanLara S. van de LandeAnne-Jet C. OostingAthanasios PapaioannouN. Owase JeelaniMaarten J. KoudstaalRoman H. KhonsariDavid J. DunawayStefanos ZafeiriouSilvia SchievanoElsevierarticlePaediatric skullMorphometricsStatistical shape model3D morphable modelSynthetic shapesDiseases of the musculoskeletal systemRC925-935ENBone Reports, Vol 15, Iss , Pp 101154- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Paediatric skull Morphometrics Statistical shape model 3D morphable model Synthetic shapes Diseases of the musculoskeletal system RC925-935
spellingShingle	Paediatric skull Morphometrics Statistical shape model 3D morphable model Synthetic shapes Diseases of the musculoskeletal system RC925-935 Eimear O' Sullivan Lara S. van de Lande Anne-Jet C. Oosting Athanasios Papaioannou N. Owase Jeelani Maarten J. Koudstaal Roman H. Khonsari David J. Dunaway Stefanos Zafeiriou Silvia Schievano The 3D skull 0–4 years: A validated, generative, statistical shape model
description	Background: This study aims to capture the 3D shape of the human skull in a healthy paediatric population (0–4 years old) and construct a generative statistical shape model. Methods: The skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population. Results: The 3DMM of the paediatric skull 0–4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases. Conclusion: This study presents a reliable statistical shape model of the paediatric skull 0–4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field.
format	article
author	Eimear O' Sullivan Lara S. van de Lande Anne-Jet C. Oosting Athanasios Papaioannou N. Owase Jeelani Maarten J. Koudstaal Roman H. Khonsari David J. Dunaway Stefanos Zafeiriou Silvia Schievano
author_facet	Eimear O' Sullivan Lara S. van de Lande Anne-Jet C. Oosting Athanasios Papaioannou N. Owase Jeelani Maarten J. Koudstaal Roman H. Khonsari David J. Dunaway Stefanos Zafeiriou Silvia Schievano
author_sort	Eimear O' Sullivan
title	The 3D skull 0–4 years: A validated, generative, statistical shape model
title_short	The 3D skull 0–4 years: A validated, generative, statistical shape model
title_full	The 3D skull 0–4 years: A validated, generative, statistical shape model
title_fullStr	The 3D skull 0–4 years: A validated, generative, statistical shape model
title_full_unstemmed	The 3D skull 0–4 years: A validated, generative, statistical shape model
title_sort	3d skull 0–4 years: a validated, generative, statistical shape model
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/e841774f1aa9415d84d62e1071bda290
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The 3D skull 0–4 years: A validated, generative, statistical shape model

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