scholarly journals Laplace-Beltrami Refined Shape Regression Applied to Neck Reconstruction for Craniosynostosis Patients

2021 ◽  
Vol 7 (2) ◽  
pp. 191-194
Author(s):  
Matthias Schaufelberger ◽  
Reinald Kühle ◽  
Frederic Weichel ◽  
Andreas Wachter ◽  
Niclas Hagen ◽  
...  
Keyword(s):  
Real Data ◽  
Statistical Shape Model ◽  
Head Model ◽  
Head Shape ◽  
Continuous Transition ◽  
Posterior Mean ◽  
Neck Reconstruction ◽  
Mean Shape ◽  
Full Head ◽  
3D Scans

Abstract This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-based classification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, most SSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace- Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes.

scholarly journals Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5345
Author(s):  
Ahmad K. Aijazi ◽  
Laurent Malaterre ◽  
Laurent Trassoudaine ◽  
Thierry Chateau ◽  
Paul Checchin
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Consensus Algorithm ◽  
3D Point Cloud ◽  
Automated Mapping ◽  
Segmentation Accuracy ◽  
Underground Infrastructure ◽  
Fuzzy C Means Clustering ◽  
3D Scans ◽  
Planning And Construction

Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than 7 % , an overall segmentation accuracy of 85 % and an overall modeling error of less than 5 % . The evaluated results not only demonstrate the efficacy but also the suitability of the proposed solution.

3D head model generation using full-face head shape measurement system

2007 ◽  
Vol 38 (13) ◽  
pp. 82-91
Author(s):  
Takehiko Koyasu ◽  
Toshiyuki Amano ◽  
Yukio Sato
Keyword(s):  
Measurement System ◽  
Shape Measurement ◽  
Head Model ◽  
Model Generation ◽  
Head Shape ◽  
Full Face ◽  
3D Head Model

scholarly journals Scalable Bias-corrected Linkage Disequilibrium Estimation Under Genotype Uncertainty

2021 ◽  
Author(s):  
David Gerard
Keyword(s):  
Linkage Disequilibrium ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Maximum Likelihood Estimators ◽  
Likelihood Estimation ◽  
Real Data ◽  
Standard Errors ◽  
Posterior Distributions ◽  
Posterior Mean ◽  
Genome Wide

AbstractLinkage disequilibrium (LD) estimates are often calculated genome-wide for use in many tasks, such as SNP pruning and LD decay estimation. However, in the presence of genotype uncertainty, naive approaches to calculating LD have extreme attenuation biases, incorrectly suggesting that SNPs are less dependent than in reality. These biases are particularly strong in polyploid organisms, which often exhibit greater levels of genotype uncertainty than diploids. A principled approach using maximum likelihood estimation with genotype likelihoods can reduce this bias, but is prohibitively slow for genome-wide applications. Here, we present scalable moment-based adjustments to LD estimates based on the marginal posterior distributions of the genotypes. We demonstrate, on both simulated and real data, that these moment-based estimators are as accurate as maximum likelihood estimators, and are almost as fast as naive approaches based only on posterior mean genotypes. This opens up bias-corrected LD estimation to genome-wide applications. Additionally, we provide standard errors for these moment-based estimators. All methods are implemented in the ldsep package on the Comprehensive R Archive Network https://cran.r-project.org/package=ldsep.

scholarly journals Determining the number of components in the PARAFAC model with a nonnegative tensor structure: A simulated EEG data study

2021 ◽  
Author(s):  
Zuzana Rošťáková ◽  
Roman Rosipal
Keyword(s):  
Data Structure ◽  
Input Parameter ◽  
Simulated Data ◽  
Real Data ◽  
Cortical Activation ◽  
Superior Performance ◽  
Head Model ◽  
Advantages And Disadvantages ◽  
Latent Components ◽  
Nonnegative Data

Background and Objective: Parallel factor analysis (PARAFAC) is a powerful tool for detecting latent components in higher-order arrays (tensors). As an essential input parameter, the number of latent components should be set in advance. However, any component number selection method already proposed in the literature became a rule of thumb. The study demonstrates the advantages and disadvantages of twelve different methods applied to well-controlled simulated data with a nonnegative structure that mimics the character of a real electroencephalogram.Methods: Existing studies have compared the methods’ performance on simulated data with a simplified structure. It was shown that the obtained results are not directly generalizable to real data. Using a real head model and cortical activation, our study focuses on nontrivial and nonnegative simulated data that resemble real electroencephalogram properties as closely as possible. Different noise levels and disruptions from the optimal structure are considered. Moreover, we validate a new method for component number selection, which we have already successfully applied to real electroencephalogram tasks. We also demonstrate that the existing approaches must be adapted whenever a nonnegative data structure is assumed. Results: We identified four methods that produce promising but not ideal results on nontrivial simulated data and present superior performance in electroencephalogram analysis practice.Conclusions: Component number selection in PARAFAC is a complex and unresolved problem. The nonnegative data structure assumption makes the problem more challenging. Although several methods have shown promising results, the issue remains open, and new approaches are needed.

scholarly journals Effects of Head Models and Dipole Source Parameters on EEG Fields

2015 ◽  
Vol 9 (1) ◽  
pp. 10-16 ◽  
Author(s):  
Li Peng ◽  
Mingming Peng ◽  
Anhuai Xu
Keyword(s):  
Source Parameters ◽  
Human Head ◽  
Basis Functions ◽  
Dipole Source ◽  
Head Model ◽  
Peak Potential ◽  
Head Shape ◽  
The Face ◽  
Pls Method ◽  
Peak Value

Head model and an efficient method for computing the forward EEG (electroencephalography)problem are essential to dipole source localization(DSL). In this paper, we use less expensive ovoid geometry to approximate human head, aiming at investigating the effects of head shape and dipole source parameters on EEG fields. The application of point least squares (PLS) based on meshless method was introduced for solving EEG forward problem and numerical simulation is implemented in three kinds of ovoid head models. We present the performances of the surface potential in the face of varying dipole source parameters in detail. The results show that the potential patterns are similar for different dipole position in different head shapes, but the peak value of potential is significantly influenced by the head shape. Dipole position induces a great effect on the peak value of potential and shift of peak potential. The degree of variation between sphere head model and non-sphere head models is seen at the same time. We also show that PLS method with the trigonometric basis is superior to the constant basis, linear basis, and quadratic basis functions in accuracy and efficiency.

Posture-invariant 3D Human Hand Statistical Shape Model

2020 ◽  
pp. 1-31
Author(s):  
Yusheng Yang ◽  
Tianyun Yuan ◽  
Toon Huysmans ◽  
Willemijn Elkhuizen ◽  
Farzam Tajdari ◽  
...  
Keyword(s):  
Human Body ◽  
Component Analysis ◽  
Kernel Functions ◽  
Statistical Shape Model ◽  
Human Hand ◽  
Shape Model ◽  
Digital Twin ◽  
3D Scan ◽  
Statistical Shape ◽  
3D Scans

Abstract A high-fidelity digital representation of the human body is a key enabler for integrating humans in a digital twin. Among different parts of human body, building the model of the hand can be a challenging task due to the posture deviations among collected scans. In this paper, we proposed a posture invariant hand statistical shape model (SSM) based on 59 3D scans of human hands. First, the 3D scans were spatially aligned using a Möbius sphere-based algorithm. An articulated skeleton, which contains 20 bone segments and 16 joints, was embedded for each 3D scan. Then all scans were aligned to the same posture using the skeleton and the linear blend skinning algorithm. Three methods, i.e. Principal Component Analysis (PCA), kernel-PCA with different kernel functions, and Independent Component Analysis, were evaluated in the construction of the SSMs regarding the compactness, the generalization ability and the specificity. The PCA-based SSM was selected, where 20 principal components were used as parameters for the model. Results of the leave-one-out validation indicate that the proposed model was able to fit a given 3D scan of the human hand at an accuracy of 1.21 ± 0.14 mm. Experiment results also indicated that the proposed SSM outperforms the SSM that was built on the scans without posture correction. It is concluded that the proposed posture correction approach can effectively improve the accuracy of the hand SSM, therefore enables its wide usage in human integrated digital twin applications.

Detecting Random Responses in a Personality Scale Using IRT-Based Person-Fit Indices

2019 ◽  
Vol 35 (1) ◽  
pp. 126-136 ◽  
Author(s):  
Tour Liu ◽  
Tian Lan ◽  
Tao Xin
Keyword(s):  
Experimental System ◽  
Real Data ◽  
Fit Indices ◽  
Random Response ◽  
Person Fit ◽  
Index Method ◽  
Single Person ◽  
Person Fit Index ◽  
Random Responses ◽  
Response Behaviors

Abstract. Random response is a very common aberrant response behavior in personality tests and may negatively affect the reliability, validity, or other analytical aspects of psychological assessment. Typically, researchers use a single person-fit index to identify random responses. This study recommends a three-step person-fit analysis procedure. Unlike the typical single person-fit methods, the three-step procedure identifies both global misfit and local misfit individuals using different person-fit indices. This procedure was able to identify more local misfit individuals than single-index method, and a graphical method was used to visualize those particular items in which random response behaviors appear. This method may be useful to researchers in that it will provide them with more information about response behaviors, allowing better evaluation of scale administration and development of more plausible explanations. Real data were used in this study instead of simulation data. In order to create real random responses, an experimental test administration was designed. Four different random response samples were produced using this experimental system.

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