title: Choroidal image analysis for OCT image sequences with applications in systemic health
Creators: name: Burke, Jamie
Publisher: name: The University of Edinburgh
Year: 2025
Subjects: subject: choroid
subject: optical coherence tomography
subject: image analysis
subject: deep learning
subject: systemic health

description: The retina is a light-sensitive tissue at the back of the eye and is responsible for vision. Light-sensitive photoreceptor cells in the outer retina detect light and, through a series of neuronal and vascular layers, process it into signals for the brain. The photoreceptors are perfused and maintained indirectly by the choroid and choriocapillaris, a highly vascularised layer posterior to the retina. The choroid is an extension of the central nervous system and has parallels with the renal cortex, but choroidal blood flow is four-fold higher per unit mass than the kidney and ten-fold higher than the brain. Thus, there has been growing interest in the structure and function of the choroidal circulation reflecting physiological status of systemic disease in the kidney and brain. The choroid can be imaged using optical coherence tomography (OCT), a non-invasive imaging technique which uses interferometry to capture three-dimensional, cross-sectional visualisations of ocular tissue at micron resolution. Advancements in OCT technology now permit deeper penetration and improved visualisation of the choroidal vessels. However, conventional methods of characterising and quantifying this vascular space have not kept pace with the improvements in OCT technology which visualise it, resulting in non-standardised manual or semi-automatic approaches as commonplace methods for choroidal measurement. The ability to measure anatomy consistently at micron-scale both intra- and inter-patient is paramount to capturing the inherent biological change or signal being studied – a signal which can be corrupted when exposed to human subjectivity. In this thesis, I develop and evaluate several novel methods to analyse the choroid in OCT image sequences, with each successive method markedly improving on its predecessors. In the first instance, I develop two semi-automatic approaches for choroid region (Gaussian Process Edge Tracing, GPET) and vessel (Multi-scale Median Cut Quantisation, MMCQ) analysis, which improve on manual approaches but ultimately are biased by the end-user's biological interpretation and technical experience. As a first step to fully automatic choroid segmentation, I develop DeepGPET as a deep learning-based method for choroid region segmentation, which significantly improves on semi-automatic approaches in terms of time, reproducibility, and end-user accessibility. However, DeepGPET lacks choroidal vessel quantification and still requires manual input for generating standardised, choroid-derived measurements. Improving on this, I developed Choroidalyzer}, a fully automatic, deep learning-based, end-to-end pipeline which fully characterises the choroidal space and vessels, and automatically generates clinically meaningful and reproducible choroid-derived metrics. I provide rigorous evaluation of these four approaches, and consider their use-case and potential clinical value in three distinct applications into systemic health: OCTANE: evaluating longitudinal choroidal change and its association with renal function in transplant recipients and donors; PREVENT: investigating associations between the choroid and risk factors for developing later-life Alzheimer's disease in a mid-life cohort; D-RISCii: assessing choroidal variation and feasibility of OCT imaging in critical care. This thesis has contributed several new approaches to the research community which are all open-source and freely available, enabling consistent and reproducible measurement of the choroid. This thesis also highlights the potential role the choroid may play in reflecting pathophysiology in the kidney, brain and wider systemic health from iatrogenic shock, thus helping accelerate the nascent field of choroidal analysis in OCT image sequences.