Duane Malcolm

An anatomical region-based statistical shape model of the human femur

We present a workflow for producing a statistical shape model (SSM) of the femur with automatically defined regions resembling general anatomic features. Explicitly defined regions enforce correspondence of anatomical features, and allow the shapes of regions to be analysed independently if needed. A training set of manually segmented femur surfaces are partitioned according to Gaussian curvature. Partitioned regions across the training set are then grouped using mean-shift clustering to identify the most stable regions into which surfaces are divided. Reference piecewise parametric meshes are designed for and fitted to each region, and used to train regional SSMs through fitting–training iterations. Fitted region meshes are assembled into full femur meshes for training a whole femur region-based SSM (rSSM). Partitioning, clustering and shape modelling results are presented for 41 femurs. In comparison to a non-regional SSM, the rSSM was more efficient and correspondent in its approximation of unseen femurs.

Automatic meshing of femur cortical surfaces from clinical CT images

We present an automated image-to-mesh workflow that meshes the cortical surfaces of the human femur, from clinical CT images. A piecewise parametric mesh of the femoral surface is customized to the in-image femoral surface by an active shape model. Then, by using this mesh as a first approximation, we segment cortical surfaces via a model of cortical morphology and imaging characteristics. The mesh is then customized further to represent the segmented inner and outer cortical surfaces. We validate the accuracy of the resulting meshes against an established semi-automated method. Root mean square error for the inner and outer cortical meshes were 0.74 mm and 0.89 mm, respectively. Mean mesh thickness absolute error was 0.03 mm with a standard deviation of 0.60 mm. The proposed method produces meshes that are correspondent across subjects, making it suitable for automatic collection of cortical geometry for statistical shape analysis.

Development of a 3D finite element model of lens microcirculation

BackgroundIt has been proposed that in the absence of a blood supply, the ocular lens operates an internal microcirculation system. This system delivers nutrients, removes waste products and maintains ionic homeostasis in the lens. The microcirculation is generated by spatial differences in membrane transport properties; and previously has been modelled by an equivalent electrical circuit and solved analytically. While effective, this approach did not fully account for all the anatomical and functional complexities of the lens. To encapsulate these complexities we have created a 3D finite element computer model of the lens.MethodsInitially, we created an anatomically-correct representative mesh of the lens. We then implemented the Stokes and advective Nernst-Plank equations, in order to model the water and ion fluxes respectively. Next we complemented the model with experimentally-measured surface ionic concentrations as boundary conditions and solved it.ResultsOur model calculated the standing ionic concentrations and electrical potential gradients in the lens. Furthermore, it generated vector maps of intra- and extracellular space ion and water fluxes that are proposed to circulate throughout the lens. These fields have only been measured on the surface of the lens and our calculations are the first 3D representation of their direction and magnitude in the lens.ConclusionValues for steady state standing fields for concentration and electrical potential plus ionic and fluid fluxes calculated by our model exhibited broad agreement with observed experimental values. Our model of lens function represents a platform to integrate new experimental data as they emerge and assist us to understand how the integrated structure and function of the lens contributes to the maintenance of its transparency.

Automatic Landmark Detection Using Statistical Shape Modelling and Template Matching

We propose a new methodology for automated landmark detection for breast MR images that combines statistical shape modelling and template matching into a single framework. The method trains a statistical shape model (SSM) of breast skin surface using 30 manually labelled landmarks, followed by generation of template patches for each landmark. Template patches are matched across the unseen image to produce correlation maps. Correlation maps of the landmarks and the shape model are used to generate a first estimate of the landmarks referred to as “shape predicted landmarks”. These landmarks are refined using local maximum search in individual landmarks correlation maps. The algorithm was validated on 30 MR images using a leave-one-out approach. The results reveal that the method is robust and capable of localising landmarks with an error of 3.41 ± 2.10 mm.

Robust Landmark Identification for Generating Subject Specific Models for Biomechanics

We propose a robust and accurate method for automatic landmark identification for torso MR images. The method combines cross-correlation and statistical models into a single framework. Principal component analysis is used to generate statistical models of the relative landmark positions, and the template images. Partial least-squares regression is used predict the initial landmark positions, and template images for the landmarks from the characteristics of the unseen MR images. The landmark template images are then cross-correlated with the search regions and the statistical model is used to constrain the search for the maximum combined correlation. The method was trained and tested using MR images from 51 female subjects. The method was able to identify the position of the tracheal bifurcation and jugular notch landmarks with a mean ± SD error of 6.1 ± 5.2 mm, with 9.1 % of the errors greater than 10 mm. This result was three times better than the standard template matching method, which gave a mean ± SD error of 18.9 ± 21.7 mm, with 33 % of the errors greater than 10 mm.

Modelling the circulation in the mammalian lens

Abstract The ocular lens generates an internal circulation in order to transport nutrients into the lens. The internal circulation enters at the poles and exits at the equator of the lens. This internal circulation is the result of the localization of membrane proteins and the cellular architecture of the lens. This work presents the development of a finite element model that solves the advection-diffusion equations for ion fluxes, the isotonic transport equations for water flux and the equations for transmembrane ion and water fluxes.