Analyzing infant head flatness and asymmetry using kernel density estimation of directional surface data from a craniofacial 3D model

Vuollo, Ville; Holmström, Lasse; Aarnivala, Henri; Harila, Virpi; Heikkinen, Tuomo; Pirttiniemi, Pertti; Valkama, Marita

doi:10.1002/sim.7032

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…The point-to-point variables oblique cranial length ratio (OCLR) and cephalic index (CI), as well as the 3D surface variables, weighted asymmetry score (wAS), and flatness score (FS) based on surface normal vector distribution, were measured from the 3D images [24] (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

A 3D Follow-Up Study of Cranial Asymmetry from Early Infancy to Toddler Age after Preterm versus Term Birth

Launonen

Aarnivala

Kyteas

et al. 2019

JCM

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Preterm infants are at higher risk for both symmetrical and asymmetrical head molding. This study involved 3D stereophotogrammetry to assess the cranial growth, molding, and incidence of deformational plagiocephaly (DP) in preterm children compared to term born children. Thirty-four preterm infants and 34 term born controls were enrolled in this study from Oulu University Hospital, Finland. Three-dimensional head images were obtained at the age of 2–4 months (T1), 5–7 months (T2), 11–13 months (T3), and 2.5–3 years (T4) from the term equivalent age (TEA). There was no statistically significant difference in oblique cranial length ratio (OCLR), cephalic index (CI), or weighted asymmetry score (wAS) between the two groups. Occipital flattening, defined by flatness score (FS) was statistically significantly greater in the preterm group than in the term group at T1–T4 (p < 0.05). In both groups, OCLR improved gradually over time. There were no instances, in either group, of severe DP and no moderate DP after T2. Results indicate that DP affects preterm and full-term children almost equally during the first three years of life, and cranial asymmetry resolves at a similar rate in both preterm and term groups after three months of corrected age. Preterm infants present with more occipital flattening than full-term children.

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Section: Methodsmentioning

confidence: 99%

A 3D Follow-Up Study of Cranial Asymmetry from Early Infancy to Toddler Age after Preterm versus Term Birth

Launonen

Aarnivala

Kyteas

et al. 2019

JCM

Self Cite

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“…Some authors have applied different mathematical processes to the classification and evaluation of cranial deformation from 3D models. Some examples are Principal Components 15 , the Root Mean Square differences between head quadrants 16 , kernel density estimation 17 , and deep learning for craniosynostosis diagnosis 14 . Furthermore, cranial deformation has been assessed by comparing the measured head shape to a fitted tri-axial ellipsoid which was considered as an ideal shape for a non-deformed cranium 18 .…”

Section: Introductionmentioning

confidence: 99%

Spherical harmonics to quantify cranial asymmetry in deformational plagiocephaly

Grieb

Barbero-García

Lerma

2022

Sci Rep

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Cranial deformation and deformational plagiocephaly (DP) in particular affect an important percentage of infants. The assessment and diagnosis of the deformation are commonly carried by manual measurements that provide low interuser accuracy. Another approach is the use of three-dimensional (3D) models. Nevertheless, in most cases, deformation measurements are carried out manually on the 3D model. It is necessary to develop methodologies for the detection of DP that are automatic, accurate and take profit on the high quantity of information of the 3D models. Spherical harmonics are proposed as a new methodology to identify DP from head 3D models. The ideal fitted ellipsoid for each head is computed and the orthogonal distances between head and ellipsoid are obtained. Finally, the distances are modelled using spherical harmonics. Spherical harmonic coefficients of degree 2 and order − 2 are identified as the correct ones to represent the asymmetry characteristic of DP. The obtained coefficient is compared to other anthropometric deformation indexes, such as Asymmetry Index, Oblique Cranial Length Ratio, Posterior Asymmetry Index and Anterior Asymmetry Index. The coefficient of degree 2 and order − 2 with a maximum degree of 4 is found to provide better results than the commonly computed anthropometric indexes in the detection of DP.

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“…Important areas of application that have stimulated much of the recent research activity in the field include bioinformatics (Boomsma et al 2008;Mardia et al 2018), astronomy (Cabella and Marinucci 2009;Marinucci and Peccati 2011), medicine (Vuollo et al 2016;Pardo et al 2017), genetics (Eisen et al 1998;Dortet-Bernadet and Wicker 2008), neurology (Gu et al 2004;Kaufman et al 2005), space situational awareness (Horwood and Poore 2014;Kent et al 2016), acoustics (McMillan et al 2013;Traa and Smaragdis 2013), image analysis (Jung et al 2011;Esteves et al 2020), text mining (Dhillon and Modha 2001;Banerjee et al 2005), machine learning (Hamsici and Martinez 2007;Sra 2018), and the modelling of wildfires (García-Portugués et al 2014;Ameijeiras-Alonso et al 2018) and sea conditions (Jona- Lasinio et al 2012Lasinio et al , 2018Lagona 2018).…”

Section: Introductionmentioning

confidence: 99%

Recent advances in directional statistics

Pewsey

García‐Portugués

2021

TEST

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Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere, and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper, we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (Wiley 1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, space situational awareness, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. AnWe are most grateful to three anonymous referees and, in alphabetical order, to

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Analyzing infant head flatness and asymmetry using kernel density estimation of directional surface data from a craniofacial 3D model

Cited by 16 publications

References 31 publications

A 3D Follow-Up Study of Cranial Asymmetry from Early Infancy to Toddler Age after Preterm versus Term Birth

A 3D Follow-Up Study of Cranial Asymmetry from Early Infancy to Toddler Age after Preterm versus Term Birth

Spherical harmonics to quantify cranial asymmetry in deformational plagiocephaly

Recent advances in directional statistics

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