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arXiv · 2412.17346

FFA Sora, video generation as fundus fluorescein angiography simulator

Xinyuan Wu·Lili Wang·Ruoyu Chen·Bowen Liu·Weiyi Zhang·Xi Yang·Yifan Feng·Mingguang He·Danli Shi
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Abstract

Fundus fluorescein angiography (FFA) is critical for diagnosing retinal vascular diseases, but beginners often struggle with image interpretation. This study develops FFA Sora, a text-to-video model that converts FFA reports into dynamic videos via a Wavelet-Flow Variational Autoencoder (WF-VAE) and a diffusion transformer (DiT). Trained on an anonymized dataset, FFA Sora accurately simulates disease features from the input text, as confirmed by objective metrics: Frechet Video Distance (FVD) = 329.78, Learned Perceptual Image Patch Similarity (LPIPS) = 0.48, and Visual-question-answering Score (VQAScore) = 0.61. Specific evaluations showed acceptable alignment between the generated videos and textual prompts, with BERTScore of 0.35. Additionally, the model demonstrated strong privacy-preserving performance in retrieval evaluations, achieving an average Recall@K of 0.073. Human assessments indicated satisfactory visual quality, with an average score of 1.570(scale: 1 = best, 5 = worst). This model addresses privacy concerns associated with sharing large-scale FFA data and enhances medical education.

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Xinyuan Wu, Lili Wang, Ruoyu Chen, Bowen Liu, Weiyi Zhang, Xi Yang, Yifan Feng, Mingguang He, Danli Shi. 2024-12-23. FFA Sora, video generation as fundus fluorescein angiography simulator. https://arxiv.org/abs/2412.17346

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