About this role
Project Supervisor - Dr YingLiang Ma
Background
Cardiovascular disease is the leading cause of death in the UK. As of 2023, over 1.6 million people in the UK have been diagnosed with heart arrhythmias, and 900,000 with heart failure. Medical imaging techniques, such as CT, MRI, and X-ray, are vital for diagnosing and guiding the treatment of cardiovascular diseases. This project aims to develop a unified artificial intelligence (AI) model capable of segmenting 3D medical images from standard clinical scans and generating 3D meshes across multiple imaging modalities. The project will also investigate automatic registration between different image modalities to guide cardiac interventional procedures. You will work within a multidisciplinary team that includes consultant cardiologists from St. Thomas’ Hospital, London. The project is partnered with King’s College London and Fudan University, China. This project is linked to an EPSRC-funded project titled "3D Hybrid Guidance System for Cardiac Interventional Procedures.
Research methodology
The unified model will leverage 50,000 patient scans from the UK Biobank imaging database, using a semi-supervised learning strategy to learn the orientation of diverse clinical scans and the anatomical structure of the heart. The model can be fine-tuned for different clinical tasks without retraining the entire network, enabling an agile workflow for performing various clinical applications. For end-to-end mesh generation, we will adopt a surface-fitting approach to produce high-quality 3D meshes, addressing challenges associated with the low spatial resolution of short-axis MR image sequences.
Training
You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural fields for 3D reconstruction. This position provides an opportunity to collaborate with scientists from partner institutes around the world. You will also receive specialized training in high-performance computing and the use of GPU clusters.
Entry Requirements
Acceptable first degree - Computer Science/Physics/Maths
The standard minimum entry requirement is 2:1.
First class in bachelor degree or a master degree.
Mode of Study
Full-time
Start Date
1 October 2026
Funding Information:
This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years. The current UEA/institutional PhD stipend rate is £19,814, and will be subject to any agreed inflationary uplift for 2026/27.