Nose approximation among South African groups from cone-beam computed tomography (CBCT) using a new computer-assisted method based on automatic landmarking.

Considering the high demand for the identification of unknown remains in South Africa, a need exists to establish reliable facial approximation techniques that will take into account sex and age and, most importantly, be useful within the South African context. This study aimed to provide accurate statistical models for predicting nasal soft-tissue shape from information about the underlying skull subtract among a South African sample. The database containing 200 cone-beam computer tomography (CBCT) scans (100 black South Africans and 100 white South Africans). The acquisition and extraction of the 3D relevant anatomical structures (hard- and soft-tissue) were performed by an automated three-dimensional (3D) method based on an automatic dense landmarking procedure using MeVisLab © v. 2.7.1 software. An evaluation of shape differences attributed to known factors (ancestry, sex, size, and age) was performed using geometric morphometric and statistical models of prediction were created using a Projection onto Latent Structures Regression (PLSR) algorithm. The accuracy of the estimated soft-tissue nose was evaluated in terms of metric deviations on training and un-trained datasets. Our findings demonstrated the influence of factors (sex, aging, and allometry) on the variability of the hard- and soft-tissue among two South African population groups. This research provides accurate statistical models optimized by including additional information such as ancestry, sex, and age. When using the landmark-to landmark distances, the prediction errors ranged between 1.769mm and 2.164mm for black South Africans at the tip of the nose and the alae, while they ranged from 2.068mm to 2.175mm for the white subsample. The prediction errors on un-trained data were slightly larger, ranging between 2.139mm and 2.833mm for the black South African sample at the tip of the nose and the alae and ranging from 2.575mm to 2.859mm for the white South African sample. This research demonstrates the utilization of an automated 3Dmethod based on an automatic landmarking method as a convenient prerequisite for providing a valid and reliable nose prediction model that meets population-specific standards for South Africans.

Separating positional noise from neutral alignment in multicomponent statistical shape models.

Given sufficient training samples, statistical shape models can provide detailed population representations for use in anthropological and computational genetic studies, injury biomechanics, musculoskeletal disease models or implant design optimization. While the technique has become extremely popular for the description of isolated anatomical structures, it suffers from positional interference when applied to coupled or articulated input data. In the present manuscript we describe and validate a novel approach to extract positional noise from such coupled data. The technique was first validated and then implemented in a multicomponent model of the lower limb. The impact of noise on the model itself as well as on the description of sexual dimorphism was evaluated. The novelty of our methodology lies in the fact that no rigid transformations are calculated or imposed on the data by means of idealized joint definitions and by extension the models obtained from them.

Automatic landmarking as a convenient prerequisite for geometric morphometrics. Validation on cone beam computed tomography (CBCT)- based shape analysis of the nasal complex.

Manual landmarking is used in several manual and semi-automated prediction guidelines for approximation of the nose. The manual placement of landmarks may, however, render the analysis less repeatable due to observer subjectivity and, consequently, have an impact on the accuracy of the human facial approximation. In order to address this subjectivity and thereby improve facial approximations, we are developing an automated three-dimensional (3D) method based on an automatic dense landmarking procedure using non-rigid surface registration. The aim of this study was to validate the automatic landmarking method by comparing the intra-observer errors (INTRA-OE) and inter-observer errors (INTER-OE) between automatic and manual landmarking. Cone beam computed tomography (CBCT) scans of adult South Africans were selected from the Oral and Dental Hospital, University of Pretoria, South Africa. In this study, the validation of the automatic landmarking was performed on 20 3D surfaces. INTRA-OE and INTER-OE were analyzed by registering 41 craniometric landmarks from 10 hard-tissue surfaces and 21 capulometric landmarks from 10 soft-tissue surfaces of the same individuals. Absolute precision of the landmark positioning (both on the samples as well as the template) was assessed by calculating the measurement error (ME) for each landmark over different observers. Systematic error (bias) and relative random error (precision) was further quantified through repeated measures ANOVA (ANOVA-RM). The analysis showed that the random component of the ME in landmark positioning between the automatic observations were on average on par with the manual observations, except for the soft-tissue landmarks where automatic landmarking showed lower ME compared to manual landmarking. No bias was observed within the craniometric landmarking methods, but some bias was observed for capulometric landmarking. In conclusion, this research provides a first validation of the precision and accuracy of the automatic placement of landmarks on 3D hard- and soft-tissue surfaces and demonstrates its utilization as a convenient prerequisite for geometric morphometrics based shape analysis of the nasal complex.

Statistical Shape Modeling of Skeletal Anatomy for Sex Discrimination: Their Training Size, Sexual Dimorphism, and Asymmetry.

Purpose: Statistical shape modeling provides a powerful tool for describing and analyzing human anatomy. By linearly combining the variance of the shape of a population of a given anatomical entity, statistical shape models (SSMs) identify its main modes of variation and may approximate the total variance of that population to a selected threshold, while reducing its dimensionality. Even though SSMs have been used for over two decades, they lack in characterization of their goodness of prediction, in particular when defining whether these models are actually representative for a given population. Methods: The current paper presents, to the authors' knowledge, the most extent lower limb anatomy shape model considering the pelvis, femur, patella, tibia, fibula, talus, and calcaneum to date. The present study includes the segmented training shapes (n = 542) obtained from 271 lower limb CT scans. The different models were evaluated in terms of accuracy, compactness, generalizability as well as specificity. Results: The size of training samples needed in each model so that it can be considered population covering was estimated to approximate around 200 samples, based on the generalizability properties of the different models. Simultaneously differences in gender and patterns in left-right asymmetry were identified and characterized. Size was found to be the most pronounced sexual discriminator whereas intra-individual variations in asymmetry were most pronounced at the insertion site of muscles. Conclusion: For models aimed at population covering descriptive studies, the number of training samples required should amount a sizeable 200 samples. The geometric morphometric method for sex discrimination scored excellent, however, it did not largely outperformed traditional methods based on discrete measures.

Skeletal dimensions as predictors for the shape of the nose in a South African sample: A cone-beam computed tomography (CBCT) study.

The profile of the nose is an important feature for facial approximations. Although several manual and semi-automated prediction guidelines exist for estimating the shape of the nose, the reliability and applicability of these methods to South Africans groups are unknown. The aim of this study was to predict the displacements of capulometric landmarks from hard-tissue planes to facilitate nasal soft-tissue reconstruction in a South African sample. Cone beam computed tomography (CBCT) scans of 120 adult South Africans were selected from the Oral and Dental Hospital, University of Pretoria, South Africa. Measurements involving craniometric and capulometric landmarks of the nose were obtained as plane-to-plane distances. Correlation coefficients between hard- and soft-tissue measurements were determined, and regression equations computed to assist in the prediction of the most probable shape and size of the nose. All hard- and soft-tissue measurements appeared significantly different between groups, except for the distance between the pronasale and nasion in the transverse plane and for the distance between the alare and the nasion in the coronal plane. The nasal height, nasal bone length and the nasal bone projection were significant predictors of the pronasale, subnasale and alare positions. More precisely, the nasal height and the nasal bone length were significant predictors of the pronasale position in both groups. Nasal bone projection was only useful for predicting shape in white South Africans. The variation in the skeletal predictors of the external shape of the nose noted between black and white South Africans and the results of the cross-validation testing emphasize the need for population specific guidelines.

Computer-based estimation of the hip joint reaction force and hip flexion angle in three different sitting configurations.

Sitting is part of our daily work and leisure activities and can be performed in different configurations. To date, the impact of different sitting configurations on hip joint loading has not been studied. We therefore evaluated the hip joint reaction force (HJRF) and hip flexion angle in a virtual representative male Caucasian population by means of musculoskeletal modelling of three distinct sitting configurations: a simple chair, a car seat and a kneeling chair configuration. The observed median HJRF in relation to body weight and hip flexion angle, respectively, was 22.3% body weight (%BW) and 63° for the simple chair, 22.5%BW and 79° for the car seat and 8.7%BW and 50° for the kneeling chair. Even though the absolute values of HJRF are low compared to the forces generated during dynamic activities, a relative reduction of over 50% in HJRF was observed in the kneeling chair configuration. Second, the hip flexion angles were both in the kneeling chair (-29°) and simple chair configuration (-16°) lower compared to the car seat and, as such, did not reach the threshold value for femoroacetabular conflict. In conclusion, the kneeling chair appears to hold the greatest potential as an ergonomic sitting configuration for the hip joint.

An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study.

BACKGROUND: Automated methods to evaluate growth of hand and wrist bones on radiographs and magnetic resonance imaging have been developed. They can be applied to estimate age in children and subadults. Automated methods require the software to (1) recognise the region of interest in the image(s), (2) evaluate the degree of development and (3) correlate this to the age of the subject based on a reference population. For age estimation based on third molars an automated method for step (1) has been presented for 3D magnetic resonance imaging and is currently being optimised (Unterpirker et al. 2015). AIM: To develop an automated method for step (2) based on lower third molars on panoramic radiographs. MATERIALS AND METHODS: A modified Demirjian staging technique including ten developmental stages was developed. Twenty panoramic radiographs per stage per gender were retrospectively selected for FDI element 38. Two observers decided in consensus about the stages. When necessary, a third observer acted as a referee to establish the reference stage for the considered third molar. This set of radiographs was used as training data for machine learning algorithms for automated staging. First, image contrast settings were optimised to evaluate the third molar of interest and a rectangular bounding box was placed around it in a standardised way using Adobe Photoshop CC 2017 software. This bounding box indicated the region of interest for the next step. Second, several machine learning algorithms available in MATLAB R2017a software were applied for automated stage recognition. Third, the classification performance was evaluated in a 5-fold cross-validation scenario, using different validation metrics (accuracy, Rank-N recognition rate, mean absolute difference, linear kappa coefficient). RESULTS: Transfer Learning as a type of Deep Learning Convolutional Neural Network approach outperformed all other tested approaches. Mean accuracy equalled 0.51, mean absolute difference was 0.6 stages and mean linearly weighted kappa was 0.82. CONCLUSION: The overall performance of the presented automated pilot technique to stage lower third molar development on panoramic radiographs was similar to staging by human observers. It will be further optimised in future research, since it represents a necessary step to achieve a fully automated dental age estimation method, which to date is not available.

The normal-equivalent: a patient-specific assessment of facial harmony.

Evidence-based practice in oral and maxillofacial surgery would greatly benefit from an objective assessment of facial harmony or gestalt. Normal reference faces have previously been introduced, but they describe harmony in facial form as an average only and fail to report on harmonic variations found between non-dysmorphic faces. In this work, facial harmony, in all its complexity, is defined using a face-space, which describes all possible variations within a non-dysmorphic population; this was sampled here, based on 400 healthy subjects. Subsequently, dysmorphometrics, which involves the measurement of morphological abnormalities, is employed to construct the normal-equivalent within the given face-space of a presented dysmorphic face. The normal-equivalent can be seen as a synthetic identical but unaffected twin that is a patient-specific and population-based normal. It is used to extract objective scores of facial discordancy. This technique, along with a comparing approach, was used on healthy subjects to establish ranges of discordancy that are accepted to be normal, as well as on two patient examples before and after surgical intervention. The specificity of the presented normal-equivalent approach was confirmed by correctly attributing abnormality and providing regional depictions of the known dysmorphologies. Furthermore, it proved to be superior to the comparing approach.

Forensic odontology in the disaster victim identification process.

Disaster victim identification (DVI) is an intensive and demanding task involving specialists from various disciplines. The forensic dentist is one of the key persons who plays an important role in the DVI human identification process. In recent years, many disaster incidents have occurred that challenged the DVI team with various kinds of difficulties related to disaster management and unique situations in each disaster. New technologies have been developed to make the working process faster and more effective and the different DVI protocols have been evaluated and improved. The aim of this article is to collate all information regarding diagnostic tools and methodologies pertaining to forensic odontological DVI, both current and future. It can be concluded that lessons learned from previous disaster incidents have helped to optimize working protocols and to develop new tools that can be applied in future DVI operation. The working procedures have been greatly improved by newly developed technologies.

The influence of sex, age and body mass index on facial soft tissue depths.

Facial soft tissue depth charts are used in the majority of forensic facial approximation methods. In the past, based on the multitude of available soft tissue depth charts, a number of hypotheses were advanced concerning the impact of sex, BMI and age on the depth of tissues. In this study, for the first time, a multivariate analysis was performed on a large-scale study on Caucasian adults to determine the "real" impact of these attributes. The calculation of a robust multiple linear regression of soft tissue thickness versus BMI, age and sex for each landmark separately, allowed us to study the impact from a statistical as well as practical point of view. Former findings were re-evaluated. Additionally, the results confirm the dominant role of BMI in the alterations of facial soft tissue thickness. However, excluding age and sex from the equation should be considered with care and can certainly not be applied to all landmarks. Finally, the regression equation allows increase in the specificity of tissue depths used in real cases by offering practitioners the possibility of calculating individual tissue depths.

Model-based segmentation using graph representations.

A generic supervised segmentation approach is presented. The object is described as a graph where the vertices correspond to landmarks points and the edges define the landmark relations. Instead of building one single global shape model, a priori shape information is represented as a concatenation of local shape models that consider only local dependencies between connected landmarks. The objective function is obtained from a maximum a posteriori criterion and is build up of localized energies of both shape and landmark intensity information. The optimization problem is discretized by searching candidates for each landmark using individual landmark intensity descriptors. The discrete optimization problem is then solved using mean field annealing or dynamic programming techniques. The algorithm is validated for hand bone segmentation from RX datasets and for 3D liver segmentation from contrast enhanced CT images.

Large-scale in-vivo Caucasian facial soft tissue thickness database for craniofacial reconstruction.

A large-scale study of facial soft tissue depths of Caucasian adults was conducted. Over a 2-years period, 967 Caucasian subjects of both sexes, varying age and varying body mass index (BMI) were studied. A user-friendly and mobile ultrasound-based system was used to measure, in about 20min per subject, the soft tissue thickness at 52 facial landmarks including most of the landmarks used in previous studies. This system was previously validated on repeatability and accuracy [S. De Greef, P. Claes, W. Mollemans, M. Loubele, D. Vandermeulen, P. Suetens, G. Willems, Semi-automated ultrasound facial soft tissue depth registration: method and validation. J. Forensic Sci. 50 (2005)]. The data of 510 women and 457 men were analyzed in order to update facial soft tissue depth charts of the contemporary Caucasian adult. Tables with the average thickness values for each landmark as well as the standard deviation and range, tabulated according to gender, age and BMI are reported. In addition, for each landmark and for both sexes separately, a multiple linear regression of thickness versus age and BMI is calculated. The lateral asymmetry of the face was analysed on an initial subset of 588 subjects showing negligible differences and thus warranting the unilateral measurements of the remaining subjects. The new dataset was statistically compared to three datasets for the Caucasian adults: the traditional datasets of Rhine and Moore [J.S. Rhine, C.E. Moore, Tables of facial tissue thickness of American Caucasoids in forensic anthropology. Maxwell Museum Technical series 1 (1984)] and Helmer [R. Helmer, Schädelidentifizierung durch elektronische bildmischung, Kriminalistik Verlag GmbH, Heidelberg, 1984] together with the most recent in vivo study by Manhein et al. [M.H. Manhein, G.A. Listi, R.E. Barsley, R. Musselman, N.E. Barrow, D.H. Ubelbaker, In vivo facial tissue depth measurements for children and adults. J. Forensic Sci. 45 (2000) 48-60]. The large-scale database presented in this paper offers a denser sampling of the facial soft tissue depths of a more representative subset of the actual Caucasian population over the different age and body posture subcategories. This database can be used as an updated chart for manual and computer-based craniofacial approximation and allows more refined analyses of the possible factors affecting facial soft tissue depth.

Radiation dose vs. image quality for low-dose CT protocols of the head for maxillofacial surgery and oral implant planning.

The goal of this study was to determine the acquisition parameters for a low-dose multi-slice CT protocol and to compare the effective dose and the image quality of this low-dose protocol with the image quality of a clinical multi-slice CT protocol, routinely used for visualisation of the head. The low-dose protocol was derived from a clinical multi-slice CT protocol by lowering mA s and kV and increasing the pitch. The low-dose protocol yielded a dose reduction from 1.5 to 0.18 mSv for a multi-slice CT scan of the whole head, whereas noise in the low-dose CT images was increased. For bone segmentation, noise could be reduced by use of a non-linear edge preserving smoothing filter. Tests on ESP and skull phantom indicated that the accuracy of the measurements on low-dose CT is acceptable for image-based planning of maxillofacial and oral implant surgery, reducing the dose by a factor of 8.

Temporal subtraction of thorax CR images using a statistical deformation model.

We propose a voxel-based nonrigid registration algorithm for temporal subtraction of two-dimensional thorax X-ray computed radiography images of the same subject. The deformation field is represented by a B-spline with a limited number of degrees of freedom, that allows global rib alignment to minimize subtraction artifacts within the lung field without obliterating interval changes of clinically relevant soft-tissue abnormalities. The spline parameters are constrained by a statistical deformation model that is learned from a training set of manually aligned image pairs using principal component analysis. Optimization proceeds along the transformation components rather then along the individual spline coefficients, using pattern intensity of the subtraction image within the automatically segmented lung field region as the criterion to be minimized and applying a simulated annealing strategy for global optimization in the presence of multiple local optima. The impact of different transformation models with varying number of deformation modes is evaluated on a training set of 26 images using a leave-one-out strategy and compared to the manual registration result in terms of criterion value and deformation error. Registration quality is assessed on a second set of validation images by a human expert rating each subtraction image on screen. In 85% of the cases, the registration is subjectively rated to be adequate for clinical use.

Automated segmentation of multiple sclerosis lesions by model outlier detection.

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expert segmentations, and between expert and automatic measurements.

Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information.

Maximization of mutual information of voxel intensities has been demonstrated to be a very powerful criterion for three-dimensional medical image registration, allowing robust and accurate fully automated affine registration of multimodal images in a variety of applications, without the need for segmentation or other preprocessing of the images. In this paper, we investigate the performance of various optimization methods and multiresolution strategies for maximization of mutual information, aiming at increasing registration speed when matching large high-resolution images. We show that mutual information is a continuous function of the affine registration parameters when appropriate interpolation is used and we derive analytic expressions of its derivatives that allow numerically exact evaluation of its gradient. Various multiresolution gradient- and non-gradient-based optimization strategies, such as Powell, simplex, steepest-descent, conjugate-gradient, quasi-Newton and Levenberg-Marquardt methods, are evaluated for registration of computed tomography (CT) and magnetic resonance images of the brain. Speed-ups of a factor of 3 on average compared to Powell's method at full resolution are achieved with similar precision and without a loss of robustness with the simplex, conjugate-gradient and Levenberg-Marquardt method using a two-level multiresolution scheme. Large data sets such as 256(2) x 128 MR and 512(2) x 48 CT images can be registered with subvoxel precision in <5 min CPU time on current workstations.

Automated model-based bias field correction of MR images of the brain.

We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed algorithm to other bias correction algorithms.

Automated model-based tissue classification of MR images of the brain.

We describe a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multispectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. We have validated the technique on simulated as well as on real MR images of the brain.

Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, Methodology and validation on normal subjects.

The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters. For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.

The EASI project--improving the effectiveness and quality of image-guided surgery.

In recent years, advances in computer technology and a significant increase in the accuracy of medical imaging have made it possible to develop systems that can assist the clinician in diagnosis, planning, and treatment. This paper deals with an area that is generally referred to as computer-assisted surgery, image-directed surgery, or image-guided surgery. We report the research, development, and clinical validation performed since January 1996 in the European Applications in Surgical Interventions (EASI) project, which is funded by the European Commission in their "4th Framework Telematics Applications for Health" program. The goal of this project is the improvement of the effectiveness and quality of image-guided neurosurgery of the brain and image-guided vascular surgery of abdominal aortic aneurysms, while at the same time reducing patient risks and overall cost. We have developed advanced prototype systems for preoperative surgical planning and intraoperative surgical navigation, and we have extensively clinically validated these systems. The prototype systems and the clinical validation results are described in this paper.