A generative model of the diseased human brain

The human brain is complex and hard to model. Neuroanatomically, the brain is comprised of many regions of interest. Each of these is responsible for certain neuroanatomical functions and/or biological processes. Neuroanatomists have spent a tremendous amount of time delineating and localising these regions of interest in heathy human brains, creating a repository of heathy human anatomy in the form of manually segmented atlases. These atlases, for example the Neuromorphometrics 35 atlas, are time consuming to create - it can take up to 1 month of human work to label a single brain from a high-resolution MRI image.

These atlases are then used by algorithms to learn the neuroanatomical location of key brain regions, and enable their automatically segmentation. Algorithms require large number of atlases, with sufficient variability to comprehensively describe the mapping between image intensity and tissue segmentation. Thus, the same time-consuming process needs to be repeated in very large numbers to enable algorithms to perform accurately. Unfortunately, these algorithms are severely hampered by the presence of pathology, as the learned neuroanatomical patter that predicts segmentations from images breaks down in the presence of abnormal tissues. In order for the algorithms to reliably cope with a new type of unseen pathology, human neuroanatomists are required to relabel a sufficient number of subjects with this pathology. Due to the need of lengthy human intervention, this process is not scalable across multiple pathologies; one would need to delineate a sufficient number of subjects for a wide range of pathologies so that algorithms can cope with such variability. Furthermore, manual segmentations are defined on images with certain contrasts, meaning that if the MRI machine is upgraded, or a new contrast is acquired, the same process of human relabelling needs to start all over again. In short, human-defined labels are not a feasible, nor scalable, solution to such labelling problems.

Rather than asking humans to segment multiple images, with multiple contrasts, and with multiple pathologies, this project proposes to reformulate the problem by learning to generate plausible human brain anatomical models (in the form of segmentations) using generative adversarial networks - i.e. GANs will learn the distribution of healthy human anatomy from previously-created manual segmentation, and will be able to generate a multitude of segmentations that are anatomically correct. A semantic network will then be used to generate any structural MRI image contrast from the synthetic segmentations, providing an unlimited source of MRI/segmentation pairs that model the intrinsic variation of human anatomy. Afterwards, pathology will be introduced into both the GAN model and the semantic synthesis model, allowing the generation of a set of labelled human brains that cover a large amount of pathologies and phenotypes without the need for a human neuroanatomist to label any more data. By decoupling the generative model of human anatomy (segmentations) from the semantic model of image synthesis, this project will also provide a way to introduce robustness to scanner and sequence upgrades, by requiring only the retraining of the semantic network.

Such a model would be a key enabling technology that would allow the introduction and deployment of AI-enabled segmentation tools and imaging biomarkers into clinical care, where subject's pathology is unknown and widely varying.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.