Neural Radiance Field (NeRF) Models for Ultrasound Images

Brief description of the context of the research including potential impact:
Neural Radiance Fields (NeRFs) are a new class of deep networks that provide photorealistic 3D reconstruction and rendering of natural scenes, given only a few input photos. The resulting renders include realistic reflections, lighting and other material properties with an accuracy that was previously not possible. Given its impressive performance, it is natural to ask whether it may apply to different input modalities.
Medical ultrasound is a relatively low-cost and accessible medical imaging hardware that can be used to flag a number of pathologies and abnormal changes, including cancer, without any radiation risk. However, the produced images are hard to interpret and so require highly trained professionals.
Allowing technicians (as opposed to only highly-trained professionals) to automatically flag abnormalities would have a high impact in early disease screening across many areas. This project will investigate the use of NeRFs to reconstruct organs in 3D from weakly-localized ultrasound images, whether in foetuses or adults. It will also explore deformable matching (e.g. with contrastive learning) to register scans and thus automatically flag abnormal tissue evolution.
Aims and Objectives:
To enhance the capabilities of the very simple and ubiquitous 2D Ultrasound scanner through software.
The software should not only aid highly trained Doctors in interpreting ultrasound images, but permit lesser trained healthcare workers to also undertake and interpret effective ultrasound scans.
The applications of this work should ideally be multiple, for example increasing detection rates of abnormalities in foetuses, or to aid detection of cancer in adults.

Novelty of the research methodology - Applying and adapting novel and cutting-edge techniques from Computer Vision to the Medical field, where images are often of much lower quality, the priorities are different and the challenges are different such as anatomical complexity and practical requirements (scanning times and acquisition rates).

Alignment to EPSRC's strategies and research areas (which EPSRC research area the project relates to):
Image and vision computing
Medical imaging

Any companies or collaborators involved - None

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.