Automated interpretation of fundus fluorescein angiography with multi-retinal vascular lesion segmentation.

Purpose: Fundus fluorescein angiography (FFA) is essential for diagnosing and managing retinal vascular diseases, yet its evaluation is time-consuming and subject to inter-observer variability. We aim to develop a deep-learning-based framework for multi-lesion segmentation in FFA images and to evaluate its primary performance on standard 55 ° images. Methods: A dataset of 428 standard 55 ° FFA images and 53 ultra-wide-field (UWF) FFA images was annotated for non-perfusion areas (NPA), microaneurysms (MA), neovascularization (NV) and laser spots. A residual U-Net framework was trained using a data-pooling strategy. The primary analysis was performed on the 55 ° test set, whereas the UWF subset was analyzed only as a preliminary exploratory observation because of its limited sample size. Performance was assessed using Dice score, Intersection over Union (IoU) and recall. Results: On 55 ° FFA images, the model achieved Dice scores of 0.65 ± 0.24 for NPA, 0.70 ± 0.13 for MA, 0.73 ± 0.23 for NV, and 0.70 ± 0.17 for laser spots. In an exploratory analysis of UWF images, overlap-based performance was lower for NPA (0.48 ± 0.21, Conclusion: This study has established a highly consistent and reproducible framework for multi-lesion segmentation in FFA images. The model may help standardize lesion quantification, reduce manual grading burden, and support future development of larger multi-center FFA datasets.