Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images
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Abstract (arXiv)
Angiography by Optical Coherence Tomography is a non-invasive retinal imaging modality of recent appearance that allows the visualization of the vascular structure at predefined depths based on the detection of the blood movement. OCT-A images constitute a suitable scenario to analyse the retinal vascular properties of regions of interest, measuring the characteristics of the foveal vascular and avascular zones. Extracted parameters of this region can be used as prognostic factors that determine if the patient suffers from certain pathologies, indicating the associated pathological degree. The manual extraction of these biomedical parameters is a long, tedious and subjective process, introducing a significant intra and inter-expert variability, which penalizes the utility of the measurements. In addition, the absence of tools that automatically facilitate these calculations encourages the creation of computer-aided diagnosis frameworks that ease the doctor's work, increasing their productivity and making viable the use of this type of vascular biomarkers. We propose a fully automatic system that identifies and precisely segments the region of the foveal avascular zone (FAZ) using a novel ophthalmological image modality as is OCT-A. The system combines different image processing techniques to firstly identify the region where the FAZ is contained and, secondly, proceed with the extraction of its precise contour. The system was validated using a representative set of 168 OCT-A images, providing accurate results with the best correlation with the manual measurements of two experts clinician of 0.93 as well as a Jaccard's index of 0.82 of the best experimental case. This tool provides an accurate FAZ measurement with the desired objectivity and reproducibility, being very useful for the analysis of relevant vascular diseases through the study of the retinal microcirculation.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Nov 2018.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 1; 2024: 0; 2025: 2.
9 papers.
- 2022Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Reviewcited by 28
- 2021BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Imagescited by 17
- 2022Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learningcited by 5
- 2021Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Surveycited by 2
- 2025Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challengecited by 1
- 2021FAZSeg: A New User-Friendly Software for Quantification of the Foveal Avascular Zonecited by 0
- 2022OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Datasetcited by 0
- 2023Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTAcited by 0
- 2025Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a reviewcited by 0
- 2025Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge3 Jun 2025
- 2025Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review20 Feb 2025
- 2023Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA17 Nov 2023
- 2022OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset26 Dec 2022
- 2022Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review27 Sep 2022
- 2022Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning23 Aug 2022
- 2021FAZSeg: A New User-Friendly Software for Quantification of the Foveal Avascular Zone22 Nov 2021
- 2021BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images10 Jul 2021
- 2021Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey30 Jun 2021