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This project will evaluate and contribute to the optimisation of a pre-CE-mark and pre-commercial HSI medical device for intraoperative surgical guidance in brain tumour surgery.

To deliver on this primary objective, the project will pursue the following aims:

This project seeks to advance the state of the art in AI-based surgical tool detection and tracking by designing novel semi-supervised and weakly-supervised approaches able to achieve robust and real-time performance.

This project aims to develop and validate deep learning models to predict, from MRI and clinical data, which tumours are likely to grow and require treatment. Thestudent will be able to focus on designing novel radiomics analysis for vestibular schwannoma while exploiting an existing fully-automated AI tumour segmentation framework. This will enable clinicians to deliver personalised and standardised management plans to individual patients and has the potential to significantly reduce the number of required surveillance scans.