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Title: Automatic scoring of X-rays in Psoriatic Arthritis
Author: Rambojun, Adwaye
ISNI:       0000 0004 9353 4203
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2020
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In this thesis, we investigate the potential for automation in the scoring process of Psoriatic Arthritis X-rays. We focus on the identification of bones structures through a latent space shape model that is driven by a Gaussian Process. We describe a top to bottom approach to designing such a model that includes the data collection and annotation process. We highlight the importance of cap- turing and modelling uncertainties associated with having automated systems in medical imaging. The main tool for this is noise models in a Bayesian setting. The main mathematical contribution we make takes the form of a shape model for which we perform an exact Bayesian marginalisation of the model parameters. These parameters include the shape and the pose. We define a dependence struc- ture that models the uncertainties present in a segmentation task. We show that the Active Appearance Model of Cootes et al. [2001] falls under our framework. We believe that this is significant as previous work has only focused on the real world performance of the models as opposed to the probabilistic interpretation. Such an interpretation is important as it allows us to better understand the model uncertainties.
Supervisor: Shaddick, Gavin ; Shardlow, Tony ; Campbell, Neill ; Tillett, Charles Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: PSORIATIC ARTHRITIS ; machine learning ; computer vision