Segmentation of stroke lesions in hospital scans for predicting language impairment outcomes

Brief description of the context of the research including potential impact

The project concerns developing a general-purpose software framework for accurate semantic segmentation of regions of stroke damage in hospital brain scans. The software should be applicable to both CT scans and MRI scans acquired with a wide variety of different MR contrasts, field strengths etc. The urgency of acute stroke treatment means the scans to be segmented were probably acquired rapidly, so are of poor quality and spatial resolution. Segmentation outputs are to be used for predicting how well stroke patients might recover lost language abilities and may help to optimise the types of therapies they receive. Information about how the work is to be applied can be found at https://www.ucl.ac.uk/ploras/.

Aims and Objectives

The specific objectives are to:
Develop usable software for fully automated and accurate semantic segmentation of stroke damage from clinical brain scans (both MRI and CT). It must work on a variety of image types and be applicable to both uni-modal and multi-modal data, which may have thick slices and contain various artifacts.

Novelty of Research Methodology

Novel machine learning and data science methods will be required because out-of-the-box CNN approaches generalise poorly to image contrasts not encountered within the training data (domain shift). The overall strategy will probably involve inverting a forward model, which requires the prior probability of patterns of stroke lesions to be encoded. An important sub-aim is therefore to develop a generative modelling framework for learning to encode these priors. We hope to identify links between more traditional medical image analysis methods and those more modern methods based upon CNNs. This may provide clues about making traditional methods more accurate and CNN methods more interpretable.

Alignment to EPSRC's strategies and research areas

The project fits well with the EPSRC healthcare technologies strategy. The result of the work is intended to be incorporated within a web-based system intended for predicting language impairment outcomes after stroke. As such, it may be applicable to determining which therapies those patients receive. The supervisory team have a track record of engagement with patients or software users, and some clinicians have expressed interest in working to get CE marking for software developed previously by the group.

Any companies or collaborators involved

No companies are to be involved directly, but a supervisory team member from the medical imaging group at Kings College is involved.

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.