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Publicações

Publicações por Aurélio Campilho

2025

Multitask Learning Approach for Foveal Avascular Zone Segmentation in OCTA Images

Autores
Melo, T; Carneiro, A; Campilho, A; Mendonça, AM;

Publicação
Pattern Recognition and Image Analysis - 12th Iberian Conference, IbPRIA 2025, Coimbra, Portugal, June 30 - July 3, 2025, Proceedings, Part II

Abstract
The segmentation of the foveal avascular zone (FAZ) in optical coherence tomography angiography (OCTA) images plays a crucial role in diagnosing and monitoring ocular diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). However, accurate FAZ segmentation remains challenging due to image quality and variability. This paper provides a comprehensive review of FAZ segmentation techniques, including traditional image processing methods and recent deep learning-based approaches. We propose two novel deep learning methodologies: a multitask learning framework that integrates vessel and FAZ segmentation, and a conditionally trained network that employs vessel-aware loss functions. The performance of the proposed methods was evaluated on the OCTA-500 dataset using the Dice coefficient, Jaccard index, 95% Hausdorff distance, and average symmetric surface distance. Experimental results demonstrate that the multitask segmentation framework outperforms existing state-of-the-art methods, achieving superior FAZ boundary delineation and segmentation accuracy. The conditionally trained network also improves upon standard U-Net-based approaches but exhibits limitations in refining the FAZ contours. © 2025 Elsevier B.V., All rights reserved.

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