VasFAZ-Net: A Hybrid Loss-Optimized E cientNet-scSE Network for Retinal Vasculature and FAZ Segmentation in OCTA
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Date
2026-05-01
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Institute of Mathematics and Computer Science
Abstract
Accurate segmentation of ne retinal vessels and the Foveal Avascular Zone (FAZ) in OCTA images remains challenging due to low contrast, noise, and class imbalance. To address this, a lightweight deep learning framework called VasFAZ-Net is proposed, which uses an E cientNet-B0 encoder, a parallel spatial and channel squeeze-and-excitation (scSE) decoder, and a hybrid Dice-Focal loss function, trained on the OCTA-500 dataset with both 3 mm and 6 mm scans. The proposed model achieved Dice scores of up to 88.48% for vessel segmentation and 97.98% for FAZ segmentation with only 8.8 million parameters and an inference time of 18.2 ms per image. These results demonstrate that the proposed method provides accurate, computationally e cient, and clinically deployable multi-target OCTA segmentation for retinal disease analysis.