The choroid, a highly vascular layer behind the retina, is an extension of the central nervous system and has parallels with the renal cortex, with blood flow far exceeding that of the brain and kidney. Thus, there has been growing interest of choroidal blood flow reflecting physiological status of systemic disease. Optical coherence tomography (OCT) enables high-resolution imaging of the choroid, but conventional analysis methods remain manual or semi-automatic, limiting reproducibility, standardisation and clinical utility. In this thesis, I develop several new methods to analyse the choroid in OCT image sequences, with each successive method improving on its predecessors. I first 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 remain user-dependent. To address this, I introduce DeepGPET, a deep learning-based region segmentation method which improves on execution time, reproducibility, and end-user accessibility, but lacks choroid vessel analysis and automatic feature measurement. Improving on this, I developed Choroidalyzer, a deep learning-based pipeline to segment the choroidal space and vessels and generate fully automatic, clinically meaningful and reproducible choroidal features. I provide rigorous evaluation of these four approaches and consider their potential clinical value in three applications into systemic health: OCTANE, assessing choroidal changes in renal transplant recipients and donors; PREVENT, exploring choroidal associations with Alzheimer's risk factors at mid-life; D-RISCii, assessing choroidal variation and feasibility of OCT in critical care. In short, this thesis contributes many open-source tools for standardised choroidal measurement and highlights the choroid's potential as a biomarker in systemic health.

翻译：脉络膜是位于视网膜后方的高度血管化层，作为中枢神经系统的延伸，其结构与肾皮质具有相似性，血流量远超大脑和肾脏。因此，脉络膜血流反映全身性疾病生理状态的潜力日益受到关注。光学相干断层扫描（OCT）能够实现脉络膜的高分辨率成像，但传统分析方法仍依赖于人工或半自动处理，限制了结果的可重复性、标准化程度及临床实用性。本论文针对OCT图像序列中的脉络膜分析开发了多种新方法，每种后续方法均在前序方法基础上进行改进。首先提出了两种半自动分析方法：用于脉络膜区域分割的高斯过程边缘追踪（GPET）及用于血管分析的多尺度中值切割量化（MMCQ），这些方法虽较人工分析有所改进，但仍依赖用户操作。为解决此问题，本文进一步提出基于深度学习的区域分割方法DeepGPET，在运行效率、可重复性及终端用户可操作性方面均有提升，但尚缺乏脉络膜血管分析及自动特征测量功能。在此基础上，最终开发出Choroidalyzer——一种基于深度学习的全自动处理流程，能够同步分割脉络膜空间与血管结构，并生成具有临床意义、可重复的自动化脉络膜特征指标。本文对这四种方法进行了严格评估，并通过三项全身健康相关研究探讨其潜在临床价值：OCTANE研究评估肾移植受者与供者的脉络膜变化；PREVENT研究探索中年期阿尔茨海默病风险因素与脉络膜的关联；D-RISCii研究评估危重症护理中脉络膜变异特征及OCT技术的可行性。简言之，本论文贡献了多种开源工具以实现标准化脉络膜测量，并揭示了脉络膜作为全身健康生物标志物的潜力。