[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118157-en":3,"doc-seo-118157-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118157,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine learning for retinal image analysis","Retinal images captured with modern ophthalmic modalities can reveal retinal vasculature and nerves, offering insight into cardio- and neurovascular disease. The work evaluates why non-invasive, fast, and low-cost retinal imaging—especially colour fundus photography (CFP) and optical coherence tomography (OCT)—is promising, while addressing the challenge that retinal data are complex and vary in quality, anatomy, and pathology. Using deep learning, the thesis presents tools for disease detection, automated analysis pipeline development, and validation on real-world primary care data, including robust fractal-dimension estimation and choroid analysis.","This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e. g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use:  \n• This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated.  \n• A copy can be downloaded for personal non-commercial research or study, without prior permission or charge.  \n• This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author.  \n• The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author.  \n• When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given.  \nMachine learning for retinal image analysis  \nJustin Engelmann  \nO  \nF  \nI N B  \nU  \nR  \nG  \nH  \nE  \nD  \nDoctor of Philosophy  \nTHE UNIVERSITY OF EDINBURGH  \n2024  \nFür meine Familie, die unentwegt an mich glaubt. FürApi, den ich immer in meinem Herzen tragen werde.  \nIn die Erd ist’s aufgenommen, Glücklich ist die Form gefüllt, Wird’s auch schön zutage kommen, Daß es Fleiß und Kunst vergilt? Wenn der Guß mißlang? Wenn die Form zersprang?  \nAch! vielleicht indem wir hoffen, Hat uns Unheil schon getroffen.  \niii  \nAbstract  \nRetinal images, images of the retina at the back of our eyes, are an important part of modern ophthalmology and further capture the retinal vasculature and nerves, which could allow insight into cardio-and neurovascular disease. This is especially promising as retinal images are non-invasive, fast-to-acquire and low-cost compared to other types of medical imaging such as brain magnetic resonance imaging. A variety of retinal imaging modalities exist, most importantly traditional colour fundus photography (CFP) and optical coherence tomography (OCT) . CFP is the most widespread type of retinal imaging and captures a true colour en-face image of the retina, typically with a field of view of around 45 degrees. OCT imaging captures the retina in depth and thus allows assessment of individual layers of the retina and – with modern methods such as Enhanced Depth Imaging – even captures the choroid, a dense vascular tissue beneath the retina. More recent modalities include OCT angiography which uses repeated OCT images to estimate blood flow and ultra-widefield fundus imaging which captures most of the retina with a field of view of around 200 degrees. Retinal imaging is already widespread and continuously proliferating: lower-cost handheld devices or smartphone addons make CFP available in lower resource settings, while once cuttingedge OCT can now be found at high-street opticians in the UK.  \nRetinal images provide a wealth of information but are complex to analyse, in part due to variations in image quality, anatomy, and retinal pathology that make traditional development of handcrafted analysis pipelines challenging. The recent decade saw great advances in machine learning methods, particularly deep learning for computer vision. Instead of manually designing a pipeline, a machine learning model is a parameterised pipeline that can be fit to training data to approximate the mapping from inputs to outputs. This approach is highly effective for many vision tasks, including classification, regression and segmentation.  \nIn this thesis, I present three themes of work using machine learning for retinal image analysis. First, using machine learning for retinal disease detection. Second, using machine learning for developing efficient and robust automated analysis pipelines for retinal imaging. And third, validating and applying these tools.  \nFor the first theme, I developed a deep learning model that can detect seven key retinal diseases in ultra-widefield pseudo-colour retinal images with very promising performance and investigate which regions of ","cbCaipNJykJHGwoQ","https://ap.wps.com/l/cbCaipNJykJHGwoQ","pdf",19143219,1,146,"English","en",105,"# Abstract\n# Lay Summary\n# Methods and Contributions\n## Retinal disease detection\n## Automated analysis pipelines and tools\n## Validation and real-world applications","[{\"question\":\"Why are retinal images important for modern ophthalmology?\",\"answer\":\"They capture retinal vasculature and nerves, enabling potential insight into cardio- and neurovascular disease. Non-invasive, fast-to-acquire, and low-cost imaging makes them particularly promising for analysis.\"},{\"question\":\"What machine learning approach is used in this thesis?\",\"answer\":\"Deep learning models are used as parameterised pipelines fitted to training data to approximate mappings from retinal image inputs to clinical or analytical outputs, supporting tasks such as detection and segmentation.\"},{\"question\":\"What tools were developed for automated retinal analysis?\",\"answer\":\"The thesis presents DART for fast, robust retinal fractal dimension estimation; DeepGPET for choroid region segmentation; and Choroidalyzer for choroid and choroidal vasculature segmentation with fovea localisation, plus QuickQual for efficient CFP quality assessment.\"}]","Machine learning for retinal image analysis | PDF",1785681935,368,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-retinal-image-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-retinal-image-analysis/118157/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are retinal images important for modern ophthalmology?","Question",{"text":75,"@type":76},"They capture retinal vasculature and nerves, enabling potential insight into cardio- and neurovascular disease. Non-invasive, fast-to-acquire, and low-cost imaging makes them particularly promising for analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approach is used in this thesis?",{"text":80,"@type":76},"Deep learning models are used as parameterised pipelines fitted to training data to approximate mappings from retinal image inputs to clinical or analytical outputs, supporting tasks such as detection and segmentation.",{"name":82,"@type":73,"acceptedAnswer":83},"What tools were developed for automated retinal analysis?",{"text":84,"@type":76},"The thesis presents DART for fast, robust retinal fractal dimension estimation; DeepGPET for choroid region segmentation; and Choroidalyzer for choroid and choroidal vasculature segmentation with fovea localisation, plus QuickQual for efficient CFP quality assessment.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]