Automated Analysis of Craniofacial Morphology Using Magnetic Resonance Images

Quantitative analysis of craniofacial morphology is of interest to scholars working in a wide variety of disciplines, such as anthropology, developmental biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI) provide excellent contrast between soft tissues. Given its three-dimensional nature, MRI represents an ideal imaging modality for the analysis of craniofacial structure in living individuals. Here we describe how T1-weighted MR images, acquired to examine brain anatomy, can also be used to analyze facial features. Using a sample of typically developing adolescents from the Saguenay Youth Study (N = 597; 292 male, 305 female, ages: 12 to 18 years), we quantified inter-individual variations in craniofacial structure in two ways. First, we adapted existing nonlinear registration-based morphological techniques to generate iteratively a group-wise population average of craniofacial features. The nonlinear transformations were used to map the craniofacial structure of each individual to the population average. Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features. Second, we employed a landmark-based approach to quantify variations in face surfaces. This approach involves: (a) placing 56 landmarks (forehead, nose, lips, jaw-line, cheekbones, and eyes) on a surface representation of the MRI-based group average; (b) warping the landmarks to the individual faces using the inverse nonlinear transformation estimated for each person; and (3) using a principal components analysis (PCA) of the warped landmarks to identify facial features (i.e. clusters of landmarks) that vary in our sample in a correlated fashion. As with the voxel-wise analysis of the deformation fields, we examined the effects of sex and age on the PCA-derived spatial relationships between facial features. Both methods demonstrated significant sexual dimorphism in craniofacial structure in areas such as the chin, mandible, lips, and nose

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning.

Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses

Longitudinal Volumetric Assessment of Ventricular Enlargement in Pet Dogs Trained for Functional Magnetic Resonance Imaging (fMRI) Studies

Background: Recent studies suggest that clinically sound ventriculomegaly in dogs could be a preliminary form of the clinically significant hydrocephalus. We evaluated changes of ventricular volumes in awake functional magnetic resonance imaging (fMRI) trained dogs with indirectly assessed cognitive abilities over time (thus avoiding the use of anaesthetics, which can alter the pressure). Our research question was whether ventricular enlargement developing over time would have any detrimental effect on staying still while being scanned; which can be extrapolated to the ability to pay attention and to exert inhibition. Methods: Seven healthy dogs, 2–8 years old at the baseline scan and 4 years older at rescan, participated in a rigorous and gradual training for staying motionless (<2 mm) in the magnetic resonance (MR) scanner without any sedation during 6 minute-long structural MR sequences. On T1 structural images, volumetric analyses of the lateral ventricles were completed by software guided semi-automated tissue-type segmentations performed with FMRIB Software Library (FSL, Analysis Group, Oxford, UK). Results and conclusion: We report significant enlargement for both ventricles (left: 47.46 %, right: 46.07 %) over time while dogs retained high levels of attention and inhibition. The results suggest that even considerable ventricular enlargement arising during normal aging does not necessarily reflect observable pathological changes in behavior

Doctor of Philosophy in Computing

dissertationStatistical shape analysis has emerged as an important tool for the quantitative analysis of anatomy in many medical imaging applications. The correspondence based approach to evaluate shape variability is a popular method, based on comparing configurations of carefully placed landmarks on each shape. In recent years, methods for automatic placement of landmarks have enhanced the ability of this approach to capture statistical properties of shape populations. However, biomedical shapes continue to present considerable difficulties in automatic correspondence optimization due to inherent geometric complexity and the need to correlate shape change with underlying biological parameters. This dissertation addresses these technical difficulties and presents improved shape correspondence models. In particular, this dissertation builds on the particle-based modeling (PBM) framework described by Joshua Cates' 2010 Ph.D. dissertation. In the PBM framework, correspondences are modeled as a set of dynamic points or a particle system, positioned automatically on shape surfaces by optimizing entropy contained in the model, with the idea of balancing model simplicity against accuracy of the particle system representation of shapes. This dissertation is a collection of four papers that extend the PBM framework to include shape regression and longitudinal analysis and also adds new methods to improve modeling of complex shapes. It also includes a summary of two applications from the field of orthopaedics. Technical details of the PBM framework are provided in Chapter 2, after which the first topic related to the study of shape change over time is addressed (Chapters 3 and 4). In analyses of normative growth or disease progression, shape regression models allow characterization of the underlying biological process while also facilitating comparison of a sample against a normative model. The first paper introduces a shape regression model into the PBM framework to characterize shape variability due to an underlying biological parameter. It further confirms the statistical significance of this relationship via systematic permutation testing. Simple regression models are, however, not sufficient to leverage information provided by longitudinal studies. Longitudinal studies collect data at multiple time points for each participant and have the potential to provide a rich picture of the anatomical changes occurring during development, disease progression, or recovery. The second paper presents a linear-mixed-effects (LME) shape model in order to fully leverage the high-dimensional, complex features provided by longitudinal data. The parameters of the LME shape model are estimated in a hierarchical manner within the PBM framework. The topic of geometric complexity present in certain biological shapes is addressed next (Chapters 5 and 6). Certain biological shapes are inherently complex and highly variable, inhibiting correspondence based methods from producing a faithful representation of the average shape. In the PBM framework, use of Euclidean distances leads to incorrect particle system interactions while a position-only representation leads to incorrect correspondences around sharp features across shapes. The third paper extends the PBM framework to use efficiently computed geodesic distances and also adds an entropy term based on the surface normal. The fourth paper further replaces the position-only representation with a more robust distance-from-landmark feature in the PBM framework to obtain isometry invariant correspondences. Finally, the above methods are applied to two applications from the field of orthopaedics. The first application uses correspondences across an ensemble of human femurs to characterize morphological shape differences due to femoroacetabular impingement. The second application involves an investigation of the short bone phenotype apparent in mouse models of multiple osteochondromas. Metaphyseal volume deviations are correlated with deviations in length to quantify the effect of cancer toward the apparent shortening of long bones (femur, tibia-fibula) in mouse models

Linear magnetic resonance imaging measurements of the hippocampal formation differ in young versus old dogs

Age-related hippocampal formation (HF) atrophy has been documented on MRI studies using volumetric analysis and visual rating scales.This retrospective cross-sectional study aimed to compare linear MRI measurements of the HF between young (1–3 years) and old (>10 years) non-brachycephalic dogs, with normal brain anatomy and cerebrospinal fluid (CSF) analysis. Right and left hippocampal formation height (HFH), height of the brain (HB) and mean HFH/HB ratio were measured by two observers on a transverse T2 fluid-attenuated inversion recovery sequence containing rostral colliculi and mesencephalic aqueduct.119 MRI studies were enrolled: 75 young and 44 old dogs. Left and right HFH were greater (p<0.0001) in young, while HB was greater in old dogs (p=0.024). Mean HFH/HB ratio was 15.66 per cent and 18.30 per cent in old and young dogs (p<0.0001). No differences were found comparing measurements between epileptic and non-epileptic dogs.Old dogs have a greater HB; this may represent the different study populations or a statistical phenomenon. Ageing affects HF linear measurements. A reduction of mean HFH/HB ratio between 18.30 per cent and 15.66 per cent should be considered a physiological age-related process of the canine lifespan. The use of mean HFH/HB ratio could be considered for quantifying brain atrophy in elderly dogs