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Developing new diabetic retinopathy biomarkers through image processing, computational modelling, and machine learning.

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  • Full or part time
    Prof A Morris
    Dr M Bernabeu
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Background

Diabetic retinopathy (DR) is the leading cause of visual loss in developed countries worldwide. Previous studies have reported structural and haemodynamic changes in the diabetic eye microvasculature that precede clinically manifested pathological alterations of the retina. Therefore, new methods that allow greater understanding of these early changes may empower earlier detection of DR helping to limit damage to the retina and vision function.
Optical Coherence Tomography Angiography (OCT-A) [1] is a non-invasive, dyeless technique capable of resolving the microvasculature of the eye to a level of detail never seen before. It is only very recently that the first OCT-A commercial products have started to be utilised in clinical centres. One of the first OCT-A devices installed in the UK is hosted in Edinburgh at the Clinical Research Imaging Centre (CRIC, a clinical research facility in partnership between the University of Edinburgh and NHS Lothian). CRIC’s Image Analysis Laboratory Manager, Dr MacGillivray, is one of the co-supervisors of this project and works closely with colleagues at Queen’s University in Belfast, where a second OCT-A machine operates. Furthermore, co-supervisors Prof. Dhillon and Dr MacGillivray have over 12 years’ experience developing a successful research programme around retinal imaging and analysis and are behind the software package VAMPIRE used in research centres worldwide [2].
In recent work [3], co-supervisor Dr Bernabeu and colleagues showed that it is possible to build computational blood flow models from high-resolution images of the parafoveal region of the retina (of paramount importance for sharp central vision and visual detail). These computational models can provide a detailed haemodynamic characterisation of the region beyond the capabilities of current imaging technologies.
The successful applicant will design and run a series of longitudinal studies aimed at identifying morphological and flow-based early indicators of the pathological changes that lead to visual loss in advanced DR stages. Furthermore, he/she will investigate the use of machine learning approaches, such as clustering and deep learning, to develop robust classifiers. We now have a unique opportunity to combine these novel technologies with the OCT-A datasets at Edinburgh and Belfast in order to realise the full potential of this patient-specific approach to achieving Precision Medicine for eye care.
The supervisory team brings together a unique set of skills in order to lead a programme of research of high calibre: Prof. Andrew Morris (Health Informatics), Dr Miguel O. Bernabeu (computational modelling in Biology and Medicine), Dr Tom MacGillivray (multimodal image acquisition and retinal image processing), and Prof Baljean Dhillon (Clinical Ophthalmology and Brain Sciences).

Aims

In this project, the student will bring together cutting edge image processing, computational modelling, and machine learning methods in order to characterise the early changes in microvascular haemodynamics associated with DR. The project will take advantage of the new OCT-A datasets in Edinburgh and Belfast, which are world-leading. The main objective is to investigate clinically relevant diabetic retinopathy biomarkers and design the computational pipelines and protocols necessary to facilitate their use in future large-scale clinical and pre-clinical studies.

Training Outcomes

The student will receive state-of-the-art training in the core disciplines of image analysis, computational modelling, statistical methods, and data science while gaining expert knowledge in the context of diabetic retinopathy. This highly interdisciplinary approach is well aligned with the “T-shaped researcher” training requirements identified as key in the DTP in Precision Medicine. The student will develop the essential soft and domain-specific skills necessary to design and implement novel quantitative and computational methods that could solve challenging problems across the entire spectrum of cardiovascular medicine both in academic and industrial settings.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note you must apply to one of the projects and you are encouraged to contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2017
 
Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or soon will obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £14,296 (RCUK rate 2016/17) for UK and EU nationals that meet all required eligibility criteria.
 
Full eligibility details are available: http://www.mrc.ac.uk/skills-careers/studentships/studentship-guidance/student-eligibility-requirements/
 
Enquiries regarding programme: [Email Address Removed]

References

[1] de Carlo et al. “A review of optical coherence tomography angiography (OCTA)” International Journal of Retina and Vitreous 2015 1:5.

[2] E. Trucco, A. Giachetti, L. Ballerini, D. Relan, A. Cavinato, T. MacGillivray, Morphometric Measurements of the Retinal Vasculature in Fundus Images with VAMPIRE, in Biomedical Image Understanding: Methods and Applications, J. Lim, S. Ong, W. Xiong, Eds., John Wiley & Sons, 2015

[3] Yang L*, Bernabeu MO*, Lammer J, Cai CC, Jones ML, Franco CA, Aiello LP, Sun JK (* equally contributing lead authors). “Computational Fluid Dynamics Assisted Characterization of Parafoveal Hemodynamics in Diabetic Retinopathy using Adaptive Optics Scanning Laser Ophthalmoscopy” Biomedical Optics Express (in press), 2016.



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