Browsing by Author "Borotikar, Bhushan"

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    Multi-object and multi-feature models of thumb anatomy for population based morphological assessment
    (2024) Farrell, Caitlin; Mutsvangwa, Tinashe; Vereecke, Evie; Borotikar, Bhushan
    The trapeziometacarpal (TMC) joint, formed by the junction of the trapezium and first metacarpal (MC1) bones, is highly susceptible to the onset and progression of osteoarthritis (OA). Although various treatment options exist to ease symptoms and slow the progression of OA, the success of these treatments is heavily reliant on the early diagnosis of the disorder. The primary aim of this research was to develop a methodology for the use of computed tomography (CT) imaging in the characterisation of biomechanical risk factors of OA in the TMC joint using data from OA-affected and control subjects. Features related to shape, pose, and intensity in the CT images of the TMC joints from the subjects were correlated to a range of biomechanical risk factors. Multi-object and multi feature-class statistical models of control and OA-affected datasets were created through the development of 3 pipelines – a registration pipeline, a correspondence pipeline, and a model building pipeline. These models were used to compare the ranges of 5 morphological anatomical measures, namely: the distances between the articular surfaces of the bones; the angle of volar beak protrusion; the angle of trapezial inclination; the surface areas of the articular facets; and the radii of curvature. In line with the respective hypotheses, the distance between the articular surfaces and the angle of volar beak protrusion were seen to decrease in OA-affected data whilst the surface areas of the articular facets were seen to increase. Contrary to their respective hypotheses, the angle of trapezial inclination was seen to decrease in OA-affected data and the radii of curvature of the articular facets showed no significant changes in morphology. This suggests certain anatomical measures may be indicative of the onset and progression of TMC OA and provides a range of values typical of both control subjects and OA-affected subjects. A third model representative of the combined OA-affected and control data was developed and used to determine the correlations between the shape and pose, and the shape and intensity feature classes. This indicated a high correlation between both the shape and intensity and the shape and pose feature classes, thus suggesting a correlation between the respective biomechanical risk factors. In summary, the results of this research suggest that a decreased distance between the articular surfaces, an increased articular surface area, and a decrease in the angle of volar beak protrusion may be indicative of the onset and progression of TMC OA. This research is limited by the relatively small size of the datasets used and thus further research is necessary to determine the variation in the trapezial angle of inclination and the change in concavity of the articular surface of the MC1. Moreover, this research serves as an explanation and demonstration of the developed pipelines in the creation of a DMFC-GPM of the TMC joint which can be applied to larger and more diverse datasets in future research to determine the correlations between the respective feature classes.
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    Towards a framework for multi class statistical modelling of shape, intensity and kinematics in medical images
    (2021) Fouefack, Jean-Rassaire; Mutsvangwa, Tinashe; Burdin, Valérie; Douglas, Tania; Borotikar, Bhushan
    Statistical modelling has become a ubiquitous tool for analysing of morphological variation of bone structures in medical images. For radiological images, the shape, relative pose between the bone structures and the intensity distribution are key features often modelled separately. A wide range of research has reported methods that incorporate these features as priors for machine learning purposes. Statistical shape, appearance (intensity profile in images) and pose models are popular priors to explain variability across a sample population of rigid structures. However, a principled and robust way to combine shape, pose and intensity features has been elusive for four main reasons: 1) heterogeneity of the data (data with linear and non-linear natural variation across features); 2) sub-optimal representation of three-dimensional Euclidean motion; 3) artificial discretization of the models; and 4) lack of an efficient transfer learning process to project observations into the latent space. This work proposes a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for multi-volumetric shapes is described. Both sampling and parametric based registration algorithms are proposed, which allow the establishment of dense correspondence across volumetric shapes (such as tetrahedral meshes) while preserving the spatial relationship between them. Next, the framework for developing statistical shape-kinematics models from in-correspondence multi-volumetric shapes embedding image intensity distribution, is presented. The framework incorporates principal geodesic analysis and a non-linear metric for modelling the spatial orientation of the structures. More importantly, as all the features are in a joint statistical space and in a continuous domain; this permits on-demand marginalisation to a region or feature of interest without training separate models. Thereafter, an automated prediction of the structures in images is facilitated by a model-fitting method leveraging the models as priors in a Markov chain Monte Carlo approach. The framework is validated using controlled experimental data and the results demonstrate superior performance in comparison with state-of-the-art methods. Finally, the application of the framework for analysing computed tomography images is presented. The analyses include estimation of shape, kinematic and intensity profiles of bone structures in the shoulder and hip joints. For both these datasets, the framework is demonstrated for segmentation, registration and reconstruction, including the recovery of patient-specific intensity profile. The presented framework realises a new paradigm in modelling multi-object shape structures, allowing for probabilistic modelling of not only shape, but also relative pose and intensity as well as the correlations that exist between them. Future work will aim to optimise the framework for clinical use in medical image analysis.
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    Towards a framework for multi class statistical modelling of shape, intensity, and kinematics in medical images
    (2021) Fouefack, Jean-Rassaire; Burdin, Valérie; Mutsvangwa, Tinashe; Borotikar, Bhushan; Douglas, Tania
    Statistical modelling has become a ubiquitous tool for analysing of morphological variation of bone structures in medical images. For radiological images, the shape, relative pose between the bone structures and the intensity distribution are key features often modelled separately. A wide range of research has reported methods that incorporate these features as priors for machine learning purposes. Statistical shape, appearance (intensity profile in images) and pose models are popular priors to explain variability across a sample population of rigid structures. However, a principled and robust way to combine shape, pose and intensity features has been elusive for four main reasons: 1) heterogeneity of the data (data with linear and non-linear natural variation across features); 2) sub-optimal representation of three-dimensional Euclidean motion; 3) artificial discretization of the models; and 4) lack of an efficient transfer learning process to project observations into the latent space. This work proposes a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for multi-volumetric shapes is described. Both sampling and parametric based registration algorithms are proposed, which allow the establishment of dense correspondence across volumetric shapes (such as tetrahedral meshes) while preserving the spatial relationship between them. Next, the framework for developing statistical shape-kinematics models from in-correspondence multi-volumetric shapes embedding image intensity distribution, is presented. The framework incorporates principal geodesic analysis and a non-linear metric for modelling the spatial orientation of the structures. More importantly, as all the features are in a joint statistical space and in a continuous domain; this permits on-demand marginalisation to a region or feature of interest without training separate models. Thereafter, an automated prediction of the structures in images is facilitated by a model-fitting method leveraging the models as priors in a Markov chain Monte Carlo approach. The framework is validated using controlled experimental data and the results demonstrate superior performance in comparison with state-of-the-art methods. Finally, the application of the framework for analysing computed tomography images is presented. The analyses include estimation of shape, kinematic and intensity profiles of bone structures in the shoulder and hip joints. For both these datasets, the framework is demonstrated for segmentation, registration and reconstruction, including the recovery of patient-specific intensity profile. The presented framework realises a new paradigm in modelling multi-object shape structures, allowing for probabilistic modelling of not only shape, but also relative pose and intensity as well as the correlations that exist between them. Future work will aim to optimise the framework for clinical use in medical image analysis.