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Optical Coherence Tomography Angiography (OCTA) can non-invasively visualize retinal vasculature, but quantification of ischaemia is difficult when oedema distorts OCTA images. This study proposes a 3D image-processing pipeline that accounts for oedema-related segmentation errors and extracts vessel metrics, including Macular Vascular Volume (MVV), to assess DMI before and after anti-VEGF treatment.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/image-processing-pipeline-for-diagnosing-diabetic-macular-ischaemia-in-diabetic-macular-oedema/460753/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/image-processing-pipeline-for-diagnosing-diabetic-macular-ischaemia-in-diabetic-macular-oedema/460753.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is diagnosing DMI important in patients with DMO?","Question",{"text":112,"@type":113},"DMI frequently occurs alongside DMO and is a critical cause of vision loss. Accurate diagnosis is necessary to assess the visual prognosis of interventions and to determine the presence and degree of DMI.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What problem does DMO cause for OCTA-based DMI quantification?",{"text":117,"@type":113},"DMO can distort OCTA images, making reliable quantification challenging. This distortion leads to segmentation errors that can degrade measurement accuracy.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the proposed pipeline improve DMI evaluation using OCTA?",{"text":121,"@type":113},"The pipeline uses a 3D methodology that quantitatively assesses macular vascular perfusion while explicitly accounting for segmentation errors caused by macular oedema.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},460753,1790790221,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nImage-processing pipeline for diagnosing diabetic macular ischaemia in diabetic macular oedema  \nXiaofan Xiong1, Maged Habib2,3, David H. Steel2,3,4, Chingning Taffeta Yamaguchi5, Tunde Peto6 & Boguslaw Obara1,2􀀍  \nDiabetic Macular Ischaemia (DMI) is a critical cause of vision loss, frequently occurring alongside Diabetic Macular Oedema (DMO) in patients with severe diabetic retinopathy. Accurate diagnosis of DMI is essential for assessing the visual prognosis of any intervention. In the context of clinical research for developing novel treatments to improve macular capillary perfusion, traditional imaging techniques often struggle to identify ischaemic areas precisely. Optical Coherence Tomography Angiography (OCTA) offers a non-invasive method for visualising retinal vasculature, providing potential improvements in diagnosis. However, reliable quantification of ischemia remains challenging, particularly in the presence of macular oedema, which can distort OCTA images. This study introducesan image-processing pipeline to evaluate DMI in patients with DMO using OCTA. Using a 3-dimensional methodology, the pipeline quantitatively assesses macular vascular perfusion, accounting for segmentation errors caused by macular oedema. Imaging data from 35 people with DMO and variable degrees of DMI were imaged using three OCTA devices (Heidelberg Spectralis, Optovue Angiovue, and Topcon Triton) and analysed using this 3D methodology. Key metrics including vessel density, skeletonized vessel density, average vessel radius, and our novel metric named Macular Vascular Volume (MVV), were extracted to assess consistency across two-time points, namely pre-and posttreatment with anti-Vascular Endothelial Growth Factor (anti-VEGF) agents and hence before and after resolution of macular oedema. Measurements with the novel process showed stability of the macular vessel metrics pre-and post-oedema resolution, compared with previously described OCTA metrics using current 2D segmentation methods. The proposed pipeline provides robust quantitative tools for evaluating DMI with DMO, with promise in clinical applications for improving diagnostic accuracy and optimising treatment outcomes.  \nOptical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that employs motion contrast imaging of erythrocytes within blood vessels to capture three-dimensional details of the retinal vasculature at different depths.  \nOCTA has proven highly effective in diagnosing and monitoring various diseases affecting the macular, including age-related macular degeneration. It is also valuable for assessing macular vascular perfusion and ischaemia in retinal vascular conditions such as diabetic maculopathy and vascular occlusions. Quantitative analysis of 3D OCTA images of the retinal vasculature is becoming essential for standardising the objective interpretation of clinical outcomes1.  \nSeveral OCTA-derived features and parameters are used to quantify the retinal microvasculature. With its ability to differentiate individual vascular plexi in the retina, OCTA provides detailed insights into retinal health and pathology2, see Fig. 1a.  \nOCTA allows en-face and cross-sectional B-scan images to be generated, providing a comprehensive view of both the retinal and choroidal vasculature. En-face OCTA offers a top-down view of vascular layers, while cross-sectional B-scans also include structural information, aiding in the localisation of the exact depth of  \n1School of Computing, Newcastle University, Newcastle upon Tyne, UK. 2Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK. 3Sunderland Eye Infirmary, National Health Service, Sunderland, UK.  \n4Northern Ophthalmic Research Institute, University of Sunderland, Sunderland, UK. 5Boehringer Ingelheim, Ingelheim am Rhein, Germany. 6Institute of Clinical Science, Queen’s University Belfast, Belfast, UK. 􀀍 email: ","cbCaivosWd4oBwXm","https://ap.wps.com/l/cbCaivosWd4oBwXm","pdf",3536026,11,"English","# Background and clinical need\n## Role of DMI and DMO in vision loss\n## Limitations of OCTA with oedema\n# Proposed image-processing pipeline\n## 3D methodology and segmentation-error handling\n## OCTA devices and patient imaging\n## Extracted quantitative metrics (including MVV)\n# Vascular metric assessment and implications\n## Pre-/post-treatment stability\n## Comparison with prior 2D segmentation approaches\n# OCTA imaging overview\n## Motion-contrast imaging and 3D retinal vasculature\n## En-face and cross-sectional views\n# OCTA-derived features and segmentation requirements","[{\"question\":\"Why is diagnosing DMI important in patients with DMO?\",\"answer\":\"DMI frequently occurs alongside DMO and is a critical cause of vision loss. Accurate diagnosis is necessary to assess the visual prognosis of interventions and to determine the presence and degree of DMI.\"},{\"question\":\"What problem does DMO cause for OCTA-based DMI quantification?\",\"answer\":\"DMO can distort OCTA images, making reliable quantification challenging. This distortion leads to segmentation errors that can degrade measurement accuracy.\"},{\"question\":\"How does the proposed pipeline improve DMI evaluation using OCTA?\",\"answer\":\"The pipeline uses a 3D methodology that quantitatively assesses macular vascular perfusion while explicitly accounting for segmentation errors caused by macular oedema.\"}]","Image-processing pipeline for diagnosing diabetic macular ischaemia in diabetic macular oedema | PDF",1790759418,28]