Samak, Zeynel A

English

Ischaemic stroke, occurs due to an interruption in blood flow to the brain tissue, is the leading cause of disability and death worldwide and in the UK. Choosing a patient with ischaemic stroke for the best treatment is a critical step toward individual treatment planning and a successful outcome, as the effectiveness of treatment depends heavily on the time to treatment. However, it remains challenging to predict treatment outcomes for individual patients due to the complexity of data and visually subtle changes in the brain 3D NCCT scan. The work presented in this thesis investigates these challenges by developing novel deep learning approaches to predict functional outcome of ischaemic stroke treatment from baseline 3D NCCT and clinical information available at hospital admission.First, a multimodal CNN-based method is introduced and trained on baseline 3D NCCT scans with and without clinical information to estimate the functional outcome (mRS scores) of patients with ischaemic stroke. To further improve the model performance, two attention modules based on SE which help a CNN network in encoding the global relationship between features both channel-wise and spatially, are incorporated into the model. The results demonstrate that including clinical information and also using attention modules improves model performance.Furthermore, as the stroke lesion evolves - spreads or suppresses - after treatment, estimating stroke progression before treatment can provide significant information about the success rate of treatment and the condition of patients in the future. To encode this information, two CNN approaches, end-to-end and multi-stage models, are proposed. In the end-to-end method, predicting mRS scores and follow-up scans (24-hour and 1-week) are performed together, whereas the multi-stage approach comprises two stages of training, predicting ischaemic stroke evolution at one week without voxel-wise annotation and predicting functional outcome at 90 days. In these approaches, follow-up scans, which are used only during training, are reconstructed from the baseline scan to encode the evolution of stroke lesion. It is shown that encoding stroke evolution information into models increases the performance of models to predict functional outcome.This thesis also presents a transformer-based multimodal method that predicts mRS scores using baseline information. This method investigates different multimodal fusion strategies and various transformer models including ViT variants and Swin transformer. Transformer models that are trained with NCCT scans and clinical information outperform CNN-based approaches

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Automatic prediction of functional outcome of patients with ischaemic stroke

https://research-information.bris.ac.uk/files/365793541/ZASamak_PhDThesis_Final_Version.pdf

Automatic prediction of functional outcome of patients with ischaemic stroke

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