Accurate reconstructions of pelvic defects and discontinuities using statistical shape models.
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ABSTRACT:
SUBMITTER: Meynen A
PROVIDER: S-EPMC7643466 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background/objectiveArtificial bone models (ABMs) are used in orthopaedics for research of biomechanics, development of implants and educational purposes. Most of the commercially available ABMs approximate the morphology of Europeans, but they may not depict the Asian anatomy. Therefore, our aim was to develop the first Asian ABM of the pelvis and compare it with the existing pelvic ABM (Synbone®; Caucasian male).MethodsOne hundred clinical computed tomography (CTs) of adult pelvises (male n = 50, female n = 50) of Malay, Chinese and Indian descent were acquired. CTs were segmented and defined landmarks were placed. Three 3D statistical pelvic model and mean models (overall, male, female) were generated. Anatomical variations were analysed using principal component analysis. To measure gender-related differences and differences to the existing ABM, distances between the anterior superior iliac spines (ASIS), the anterior inferior iliac spines (AIIS), the promontory and the symphysis (conjugate vera, CV) as well as the ischial spines (diameter transversa, DT) were quantified.ResultsPrincipal component analysis displayed large variability regarding the pelvic shape and size. Female and male statistical models were similar in ASIS (225 ± 20; 227 ± 13 mm; P = 0.4153) and AIIS (185 ± 11; 187 ± 10 mm; P = 0.3982) and differed in CV (116 ± 10; 105 ± 10 mm; P < 0.0001) and DT (105 ± 7; 88 ± 8 mm; P < 0.0001). Comparing the unisex mean model with the pre-existing ABM, the ASIS (226; 275 mm; P < 0.0001), the AIIS (186; 209 mm; P < 0.0001) and the CV (111; 105 mm; P < 0.0001) differed significantly. Both models were similar regarding DT (97; 95 mm; P = 0.6927). The analysis revealed notable gender- and size-dependent anatomical variations within the Asian population. Chinese, Malay and Indian descents did not differ notably. The overall Asian model was smaller than the existing ABM.The translation potential of this articleOwing to the large differences between the Asian ABM and the pre-existing ABM, as well as differences between genders, the use of an Asian- and gender-specific ABM is important to consider in research, biomechanics and implant development for this population.
| S-EPMC6939105 | biostudies-literature
Project description:BackgroundAccurate inference of genetic discontinuities between populations is an essential component of intraspecific biodiversity and evolution studies, as well as associative genetics. The most widely-used methods to infer population structure are model-based, Bayesian MCMC procedures that minimize Hardy-Weinberg and linkage disequilibrium within subpopulations. These methods are useful, but suffer from large computational requirements and a dependence on modeling assumptions that may not be met in real data sets. Here we describe the development of a new approach, PCO-MC, which couples principal coordinate analysis to a clustering procedure for the inference of population structure from multilocus genotype data.Methodology/principal findingsPCO-MC uses data from all principal coordinate axes simultaneously to calculate a multidimensional "density landscape", from which the number of subpopulations, and the membership within subpopulations, is determined using a valley-seeking algorithm. Using extensive simulations, we show that this approach outperforms a Bayesian MCMC procedure when many loci (e.g. 100) are sampled, but that the Bayesian procedure is marginally superior with few loci (e.g. 10). When presented with sufficient data, PCO-MC accurately delineated subpopulations with population F(st) values as low as 0.03 (G'(st)>0.2), whereas the limit of resolution of the Bayesian approach was F(st) = 0.05 (G'(st)>0.35).Conclusions/significanceWe draw a distinction between population structure inference for describing biodiversity as opposed to Type I error control in associative genetics. We suggest that discrete assignments, like those produced by PCO-MC, are appropriate for circumscribing units of biodiversity whereas expression of population structure as a continuous variable is more useful for case-control correction in structured association studies.
| S-EPMC2625398 | biostudies-literature
Project description:Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee-production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.
| S-EPMC6460747 | biostudies-literature
Project description:Background and purposeNeurosurgical interventions of the thalamus rely on transferring stereotactic coordinates from an atlas onto the patient's MR brain images. We propose a prototype application for performing thalamus target map individualization by fusing patient-specific thalamus geometric information and diffusion tensor tractography.Materials and methodsPreviously, our workgroup developed a thalamus atlas by fusing anatomic information from 7 histologically processed thalami. Thalamocortical connectivity maps were generated from DTI scans of 40 subjects by using a previously described procedure and were mapped to a standard neuroimaging space. These data were merged into a statistical shape model describing the morphologic variability of the thalamic outline, nuclei, and connectivity landmarks. This model was used to deform the atlas to individual images. Postmortem MR imaging scans were used to quantify the accuracy of nuclei predictions.ResultsReliable tractography-based markers were located in the ventral lateral thalamus, with the somatosensory connections coinciding with the VPLa and VPLp nuclei; and motor/premotor connections, with the VLpv and VLa nuclei. Prediction accuracy of thalamus outlines was higher with the SSM approach than the ACPC alignment of data (0.56 mm versus 1.24; Dice overlap: 0.87 versus 0.7); for individual nuclei: 0.65 mm, Dice: 0.63 (SSM); 1.24 mm, Dice: 0.4 (ACPC).ConclusionsPrevious studies have already applied DTI to the thalamus. As a further step in this direction, we demonstrate a hybrid approach by using statistical shape models, which have the potential to cope with intersubject variations in individual thalamus geometry.
| S-EPMC7965575 | biostudies-literature
Project description:Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods.
| S-EPMC6399126 | biostudies-literature
Project description:BackgroundThree-dimensional (3D) scanning is an established method of breast volume estimation. However, this method can never be entirely precise, since the thoracic wall cannot be imaged by the surface scanner. Current methods rely on interpolation of the posterior breast border from the surrounding thoracic wall. Here, we present a novel method to calculate the posterior border and increase the accuracy of the measurement.MethodsUsing principal component analysis, computed tomography images were used to build a statistical shape model (SSM) of the thoracic wall. The model was fitted to 3D images and the missing thoracic wall curvature interpolated (indirect volumetry). The calculations were evaluated by ordinary least squares regression between the preoperative and postoperative volume differences and the resection weights in breast reduction surgery (N = 36). Also, an SSM of the breast was developed, allowing direct volumetry. Magnetic-resonance images (MRI) and 3D scans were acquired from 5 patients in order to validate the direct 3D volumetry.ResultsVolumetry based on a SSM exhibited a higher determination coefficient (R2 = 0,737) than the interpolation method (R2 = 0,404). The methods were not equivalent (p = 0.75), suggesting that the methods significantly differ. There was no influence of BMI on the correlation in either method. The MRI volumetry had a strong correlation with the 3D volumetry (R2 = 0,978).ConclusionThe SSM-based method of posterior breast border calculation is reliable and superior to the currently used method of interpolation. It should serve as a basis of software applications aiming at calculation of breast volume from 3D surface scanning data.
| S-EPMC7685503 | biostudies-literature
Project description:A quantitative analysis of any environment older than the instrumental record relies on proxies. Uncertainties associated with proxy reconstructions are often underestimated, which can lead to artificial conflict between different proxies, and between data and models. In this paper, using ordinary least squares linear regression as a common example, we describe a simple, robust and generalizable method for quantifying uncertainty in proxy reconstructions. We highlight the primary controls on the magnitude of uncertainty, and compare this simple estimate to equivalent estimates from Bayesian, nonparametric and fiducial statistical frameworks. We discuss when it may be possible to reduce uncertainties, and conclude that the unexplained variance in the calibration must always feature in the uncertainty in the reconstruction. This directs future research toward explaining as much of the variance in the calibration data as possible. We also advocate for a "data-forward" approach, that clearly decouples the presentation of proxy data from plausible environmental inferences.
| S-EPMC9285564 | biostudies-literature
Project description:Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences. When statistical models become more complex, it becomes harder to evaluate the role of explanatory variables on the response. For example, the interpretation and communication of the effect of predictors in regression models where interactions or smoothing splines are included can be challenging. Informative graphical representations of statistical models play a critical translational role; static nomograms are one such useful tool to visualise statistical models. In this paper, we propose the use of dynamic nomogram as a translational tool which can accommodate models of increased complexity. In theory, all models appearing in the literature could be accompanied by the corresponding dynamic nomogram to translate models in an informative manner. The R package presented will facilitate this communication for a variety of linear and non-linear models.
| S-EPMC6857916 | biostudies-literature
Project description:Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.
| S-EPMC8049237 | biostudies-literature
Project description:BackgroundMorphometric image analysis enables the quantification of differences in the shape and size of organs between individuals.MethodsHere we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes.ResultsWe found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease.ConclusionsThe results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.
| S-EPMC10775563 | biostudies-literature