3D Reconstruction and Measurement of Finger Lesions as an Outcome Measure for Systemic Sclerosis-Related Digital Ulceration

This is a self-funded opportunity.

Project advert

Digital ulcers are common in patients with systemic sclerosis (SSc), occurring in up to 50% of patients, and often early in the disease course. They can be very difficult to heal, and often become infected, sometimes with underlying osteomyelitis (bone infection) which can necessitate amputation.

Clinical trials assessing SSc-related digital ulcers have been hampered by a lack of reliable outcome measures of healing. The current treatment decision was dependent on the clinician, which could lead to surgery or simply medication. Recent studies showed that patients with SSc could collect high-quality smartphone images of their digital ulcers as a first step in a smartphone-based outcome measure.

We are looking for candidates to research non-invasive 3D reconstruction techniques (photogrammetry) to reconstruct difficult human structures, such as SSc lesions on hands. This will then be added to a newly create mobile application for SSc patients to use.

The PhD candidate will benefit from training at Manchester Metropolitan University within our brand-new facilities in the £117M Dalton Building – the Faculty of Science and Engineering’s new home. They will also gain insight from the Scleroderma and Raynaud’s Research Group within the Salford Royal NHS Foundation Trust.

Project aims and objectives

The aim of this project is to accurately reconstruct hands of systemic sclerosis patient with digital ulcers using computer vision and non-invasive photogrammetry. To achieve this aim, the main objectives are:

1. 3D reconstruct human hands using photogrammetry;
2. Evaluate accuracy of photogrammetry against LiDAR-based reconstruction;
3. Create a mobile application that can photograph hands of SSc patients and reconstruct with ulcerations;
4. Accurately measure digital ulcerations using reconstructed hands and novel AI algorithms;
5. Validate performance of the 3D reconstructed AI measurements in a clinical setting and against manual clinician annotation.

Specific requirements of the candidate

Candidates must have a strong motivation for research and excellent programming skills.

Experience in mobile application development and 3D reconstruction techniques is essential. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image analysis.

Qualifications

• A high-grade undergraduate degree (first class or upper second) in Computer Science or MSc in related field.

Skills

• Mobile application development (.NET MAUI or similar).
• 3D reconstruction (photogrammetry).
• Knowledge of software development and programming.
• Good communication and writing skills.
• Developing image analysis/machine learning algorithms would be beneficial.
• Able to work as part of a joint academia and clinical team.

How to apply

Interested applicants should contact Dr Adrian Davison for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal and aNarrative CV (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to pgradmissions@mmu.ac.uk.

Closing date: 14 October 2024. Expected start date: January 2025 for Home students and April 2025 for International students. 

Please quote the reference: SciEng-2024-Digital-Ulceration