scholarly journals A three-dimensional statistical shape model of the growing mandible

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. Klop ◽  
A. G. Becking ◽  
C. Klop ◽  
J. H. Koolstra ◽  
N. H. J. Lobé ◽  
...  
Keyword(s):  
Maxillofacial Surgery ◽  
Three Dimensional ◽  
Principal Component ◽  
Statistical Shape Model ◽  
Total Variance ◽  
Oral And Maxillofacial Surgery ◽  
Data Set ◽  
Shape Model ◽  
Computed Tomography Scanning ◽  
Statistical Shape

AbstractMandibular growth and morphology are important topics in the field of oral and maxillofacial surgery. For diagnostic and planning purposes, a normative database or statistical shape model of the growing mandible can be of great benefit. A collection of 874 cadaveric children’s mandibles with dental age between 1 and 12 years old were digitized using computed tomography scanning and reconstructed to three-dimensional models. Point correspondence was achieved using iterative closest point and coherent point drift algorithms. Principal component analysis (PCA) was applied to find the main modes of variation in the data set. The average mandible was presented, along with the first ten PCA modes. The first mode explained 78% of the total variance; combining the first ten modes accumulated to 95% of the total variance. The first mode was strongly correlated with age and hence, with natural growth. This is the largest study on three-dimensional mandibular shape and development conducted thus far. The main limitation is that the samples lack information such as gender and cause of death. Clinical application of the model first requires validation with contemporary samples.

scholarly journals Statistical Shape Modeling of Femurs Using Morphing and Principal Component Analysis

2010 ◽  
Vol 4 (2) ◽  
Author(s):  
Najah Hraiech ◽  
Christelle Boichon ◽  
Michel Rochette ◽  
Thierry Marchal ◽  
Marc Horner
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Shape Model ◽  
Training Data ◽  
Large Database ◽  
Data Set ◽  
Shape Model ◽  
Statistical Shape ◽  
Low Dimensional

In this paper, we describe a method for automatically building a statistical shape model by applying a morphing method and a principal component analysis (PCA) to a large database of femurs. One of the major challenges in building a shape model from a training data set of 3D objects is the determination of the correspondence between different shapes. In our work, we solve this problem by using a morphing method. The morphing method consists of deforming the same template mesh over a large database of femur geometries, which results in isotopological meshes and one to one correspondences; i.e., the resulting meshes have the same number of nodes, the same number of elements, and the same connectivity in all morphed meshes. By applying the morphing-based registration followed by PCA to a large database of femurs, we demonstrate that the method can be used to derive a low dimensional representation of the main variabilities of the femur geometry.

The three-dimensional shape symmetry of the lunate and its implications

2021 ◽  
pp. 175319342110040
Author(s):  
Nazlı Tümer ◽  
Olivier Hiemstra ◽  
Yvonne Schreurs ◽  
Gerald A. Kraan ◽  
Johan van der Stok ◽  
...  
Keyword(s):  
Intraclass Correlation ◽  
Three Dimensional ◽  
Dorsal Side ◽  
Statistical Shape Model ◽  
Patient Specific ◽  
Shape Model ◽  
Patient Specific Implants ◽  
Statistical Shape ◽  
Dimensional Shape ◽  
Three Dimensional Shape

We studied the three-dimensional (3-D) shape variations and symmetry of the lunate to evaluate whether a contralateral shape-based approach to design patient-specific implants for treatment of Kienböck’s disease is accurate. A 3-D statistical shape model of the lunate was built using the computed tomography scans of 54 lunate pairs and shape symmetry was evaluated based on an intraclass correlation analysis. The lunate shape was not bilaterally symmetrical in (1) the angle scaphoid surface – radius-ulna surface, (2) the dorsal side and the length of the side adjacent to the triquetrum, (3) the orientation of the volar surface, (4) the width of the side adjacent to the scaphoid, (5) the skewness in the coronal plane and (6) the curvature of bone articulating with the hamate and capitate. These findings suggest that using the contralateral lunate to design patient-specific lunate implants may not be as accurate as it is intended.

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

2018 ◽  
Vol 5 (12) ◽  
pp. 181558 ◽  
Author(s):  
William Keustermans ◽  
Toon Huysmans ◽  
Femke Danckaers ◽  
Andrzej Zarowski ◽  
Bert Schmelzer ◽  
...  
Keyword(s):  
Principal Components ◽  
Demographic Data ◽  
Principal Component ◽  
Statistical Shape Model ◽  
High Quality ◽  
Shape Model ◽  
Shape Modelling ◽  
Morphological Variations ◽  
Statistical Shape ◽  
Human Nose

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.

scholarly journals Three-dimensional MRI-based statistical shape model and application to a cohort of knees with acute ACL injury

2015 ◽  
Vol 23 (10) ◽  
pp. 1695-1703 ◽  
Author(s):  
V. Pedoia ◽  
D.A. Lansdown ◽  
M. Zaid ◽  
C.E. McCulloch ◽  
R. Souza ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Acl Injury ◽  
Statistical Shape Model ◽  
Shape Model ◽  
Statistical Shape

scholarly journals Prediction of normal bone anatomy for the planning of corrective osteotomies of malunited forearm bones using a three-dimensional statistical shape model

2017 ◽  
Vol 35 (12) ◽  
pp. 2630-2636 ◽  
Author(s):  
Flavien Mauler ◽  
Christoph Langguth ◽  
Andreas Schweizer ◽  
Lazaros Vlachopoulos ◽  
Tobias Gass ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Statistical Shape Model ◽  
Shape Model ◽  
Normal Bone ◽  
Statistical Shape ◽  
Forearm Bones

Computation of a Probabilistic Statistical Shape Model in a Maximum-a-posteriori Framework

2009 ◽  
Vol 48 (04) ◽  
pp. 314-319 ◽  
Author(s):  
X. Pennec ◽  
J. Ehrhardt ◽  
N. Ayache ◽  
H. Handels ◽  
H. Hufnagel
Keyword(s):  
Fundamental Problem ◽  
Statistical Shape Model ◽  
Training Data ◽  
Maximum A Posteriori ◽  
Model Parameters ◽  
A Posteriori ◽  
Data Set ◽  
Shape Model ◽  
Generalization Ability ◽  
Statistical Shape

Summary Objectives: When analyzing shapes and shape variabilities, the first step is bringing those shapes into correspondence. This is a fundamental problem even when solved by manually determining exact correspondences such as landmarks. We developed a method to represent a mean shape and a variability model for a training data set based on probabilistic correspondence computed between the observations. Methods: First, the observations are matched on each other with an affine transformation found by the Expectation-Maximization Iterative-Closest-Points (EM-ICP) registration. We then propose a maximum-a-posteriori (MAP) framework in order to compute the statistical shape model (SSM) parameters which result in an optimal adaptation of the model to the observations. The optimization of the MAP explanation is realized with respect to the observation parameters and the generative model parameters in a global criterion and leads to very efficient and closed-form solutions for (almost) all parameters. Results: We compared our probabilistic SSM to a SSM based on one-to-one correspondences and the PCA (classical SSM). Experiments on synthetic data served to test the performances on non-convex shapes (15 training shapes) which have proved difficult in terms of proper correspondence determination. We then computed the SSMs for real putamen data (21 training shapes). The evaluation was done by measuring the generalization ability as well as the specificity of both SSMs and showed that especially shape detail differences are better modeled by the probabilistic SSM (Hausdorff distance in generalization ability ≈ 25% smaller). Conclusions: The experimental outcome shows the efficiency and advantages of the new approach as the probabilistic SSM performs better in modeling shape details and differences.

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