Medical image segmentation and analysis using statistical shape modelling and inter-landmark relationships

The study of anatomical morphology is of great importance to medical imaging, with applications varying from clinical diagnosis to computer-aided surgery. To this end, automated tools are required for accurate extraction of the anatomical boundaries from the image data and detailed interpretation of morphological information. This thesis introduces a novel approach to shape-based analysis of medical images based on Inter- Landmark Descriptors (ILDs). Unlike point coordinates that describe absolute position, these shape variables represent relative configuration of landmarks in the shape. The proposed work is motivated by the inherent difficulties of methods based on landmark coordinates in challenging applications. Through explicit invariance to pose parameters and decomposition of the global shape constraints, this work permits anatomical shape analysis that is resistant to image inhomogeneities and geometrical inconsistencies. Several algorithms are presented to tackle specific image segmentation and analysis problems, including automatic initialisation, optimal feature point search, outlier handling and dynamic abnormality localisation. Detailed validation results are provided based on various cardiovascular magnetic resonance datasets, showing increased robustness and accuracy.Open acces

Copula models for epidemiological research and practice

Investigating associations between random variables (rvs) is one of many topics in the heart of statistical science. Graphical displays show emerging patterns between rvs, and the strength of their association is conventionally quantified via correlation coefficients. When two or more of these rvs are thought of as outcomes, their association is governed by a joint probability distribution function (pdf). When the joint pdf is bivariate normal, scalar correlation coefficients will produce a satisfactory summary of the association, otherwise alternative measures are needed. Local dependence functions, together with their corresponding graphical displays, quantify and show how the strength of the association varies across the span of the data. Additionally, the multivariate distribution function can be explicitly formulated and explored. Copulas model joint distributions of varying shapes by combining the separate (univariate) marginal cumulative distribution functions of each rv under a specified correlation structure. Copula models can be used to analyse complex relationships and incorporate covariates into their parameters. Therefore, they offer increased flexibility in modelling dependence between rvs. Copula models may also be used to construct bivariate analogues of centiles, an application for which few references are available in the literature though it is of particular interest for many paediatric applications. Population centiles are widely used to highlight children or adults who have unusual univariate outcomes. Whilst the methodology for the construction of univariate centiles is well established there has been very little work in the area of bivariate analogues of centiles where two outcomes are jointly considered. Conditional models can increase the efficiency of centile analogues in detection of individuals who require some form of intervention. Such adjustments can be readily incorporated into the modelling of the marginal distributions and of the dependence parameter within the copula model

Cardiovascular magnetic resonance of the arterial wall

BACKGROUND Atherosclerosis is the single greatest cause of mortality and morbidity in the developed world. Cardiovascular magnetic resonance (CMR) is a non-invasive imaging technique which can interrogate the arterial wall and identify atherosclerotic disease. CMR can provide quantitative volumetric data of atherosclerosis burden which have begun to be used in clinical trials, however comparatively few studies have been performed. We aimed to validate this approach ex vivo, to use it to characterise a normal population in vivo, to further develop the methodology, and to apply the technique to novel ‘at risk’ populations. METHODS AND RESULTS We validated quantitative CMR arterial wall volume in post mortem carotid arteries against both a CMR comparator, and against histological data. For all correlations, R2 was greater than 0.95: (CMR v histology: lumen volume 354 vs 308mm3, p<0.01; arterial wall volume 388 vs 351 mm3, p<0.01; total volume 750 vs 665 mm3; p<0.01). We studied 100 normal subjects from age 20 to 69 to determine normal ranges and the effect of normal ageing. Wall volume and total vessel volume increased significantly with age in both sexes (p < 0.006), and this was more marked in males. The W/OW ratio also increased significantly with age (p < 0.001). We showed that a 3-dimensional CMR sequence performs 63% faster than a conventional 2-dimensional sequence, with twice the signal-to-noise ratio (SNR), and highly correlated results (vessel volume: difference = 1.7%, R2 = 0.93, p < 0.001; lumen volume: difference = 4.9%, R2 = 0.92, p < 0.001, wall volume: difference = 4.7%, R2 = 0.77, p < 0.001, W/OW ratio: difference = 5.8%, R2 = 0.30, p < 0.001). Finally, we characterised atherosclerotic burden and arterial health in two populations with Takayasu’s arteritis and systemic lupus erythematosus by CMR. Carotid arterial wall volume was elevated in both populations: TA = 1045mm3, SLE = 761mm3, normals = 640mm3, p < 0.001, and myocardial late gadolinium enhancement was found 27% of TA patients, and in 60% of those with SLE. CONCLUSIONS Cardiovascular magnetic resonance of the arterial wall is an accurate way of measuring carotid atherosclerosis burden. This thesis validates this approach, and provides valuable normal data. It compares new techniques with old, aiding technical development. Finally, it demonstrates how the technique can be used in practice in populations with accelerated atherosclerosis. These data indicate that arterial wall CMR is ready to be applied in larger clinical trials