A Generic Image Segmentation Platform for Novel Feature Exploration in Multimodal MR Imaging using Minimally Supervised Machine Learning

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This PhD will develop novel AI approaches to image segmentation to explore meaningful clinical features in multimodal Magnetic Resonance Imaging (MRI). We will drive this by tackling several important imaging scenarios characterised by small datasets and challenging image quality. Often in advanced MRI we can visually identify a feature of interest and segment it manually with a-priori knowledge about anatomical features and expected MR contrast. However, this can be extremely difficult and time consuming, particularly when we are interested in 'difficult' to segment features, for instance very small objects with low contrast, or objects that have ill-defined anatomy or variable image contrast. For example, layer 4 of the cortex is this very fine continuous layer that runs through much of the grey matter of the brain and is pivotal in furthering our understanding of brain function and dysfunction. However, it is hard to distinguish reliably in all subjects and there are currently no methods to automatically identify it from MR images. The aim of this PhD project is to:- Develop machine learning methods to segment the tissue of interest with minimal high-level supervision (e.g. shape, topology, connectivity etc.).- Supplement the machine learning results with model-based approach development where appropriate, e.g. to ensure the results maintain a complete surface rather than broken patches.- Optimise the multimodal MRI acquisition, informed by initial AI results to maximize the efficiency of the automatic segmentation, in terms of image quality and data harmonisation.