{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-segmentation-of-the-foveal","title":"Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images","arxiv_id":"1811.10374","date":"2018-11-26","proceeding":null,"authors":["Macarena Díaz","Jorge Novo","Paula Cutrín","Francisco Gómez-Ulla","Manuel G. Penedo","Marcos Ortega"],"abstract":"Angiography by Optical Coherence Tomography is a non-invasive retinal imaging\nmodality of recent appearance that allows the visualization of the vascular\nstructure at predefined depths based on the detection of the blood movement.\nOCT-A images constitute a suitable scenario to analyse the retinal vascular\nproperties of regions of interest, measuring the characteristics of the foveal\nvascular and avascular zones. Extracted parameters of this region can be used\nas prognostic factors that determine if the patient suffers from certain\npathologies, indicating the associated pathological degree. The manual\nextraction of these biomedical parameters is a long, tedious and subjective\nprocess, introducing a significant intra and inter-expert variability, which\npenalizes the utility of the measurements. In addition, the absence of tools\nthat automatically facilitate these calculations encourages the creation of\ncomputer-aided diagnosis frameworks that ease the doctor's work, increasing\ntheir productivity and making viable the use of this type of vascular\nbiomarkers.\n  We propose a fully automatic system that identifies and precisely segments\nthe region of the foveal avascular zone (FAZ) using a novel ophthalmological\nimage modality as is OCT-A. The system combines different image processing\ntechniques to firstly identify the region where the FAZ is contained and,\nsecondly, proceed with the extraction of its precise contour. The system was\nvalidated using a representative set of 168 OCT-A images, providing accurate\nresults with the best correlation with the manual measurements of two experts\nclinician of 0.93 as well as a Jaccard's index of 0.82 of the best experimental\ncase. This tool provides an accurate FAZ measurement with the desired\nobjectivity and reproducibility, being very useful for the analysis of relevant\nvascular diseases through the study of the retinal microcirculation.","url_abs":"http://arxiv.org/abs/1811.10374v1","url_pdf":"http://arxiv.org/pdf/1811.10374v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"octagon","name":"OCTAGON","full_name":"OCTAGON Dataset"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}