从非侵入到侵入：利用基准数据集和新颖网络增强从CFP合成FFA
Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network
AI总结：
本文提出扩散引导生成对抗网络，通过动态扩散判别器和类别感知增强器从非侵入性CFP合成多疾病FFA图像，并构建首个多疾病配对数据集MPOS，实验证明合成图像可提升眼底疾病诊断准确率。
摘要：
眼底成像在眼科中是一项关键工具，不同的成像模态各有其特定优势。例如，眼底荧光素血管造影（FFA）能够独特地提供关于视网膜血管动态和病理的详细洞察，在检测微血管异常和灌注状态方面优于彩色眼底照片（CFP）。然而，传统的侵入性FFA由于需要注射荧光素染料而伴随不适和风险，因此从非侵入性CFP合成FFA图像既有意义又具有挑战性。以往的研究主要聚焦于单一疾病类别中的FFA合成。在本工作中，我们通过设计一个扩散引导的生成对抗网络来探索多种疾病中的FFA合成，该网络在判别器中引入了自适应且动态的扩散前向过程，并添加了一个类别感知表示增强器。此外，为了促进这项研究，我们收集了首个多疾病CFP和FFA配对数据集，命名为多疾病配对眼部合成（MPOS）数据集，包含四种不同的眼底疾病。实验结果表明，与最先进的方法相比，我们的FFA合成网络能够生成更好的FFA图像。进一步地，我们引入了一个配对模态诊断网络来验证合成FFA图像在多种眼底疾病诊断中的有效性，结果显示，我们合成的FFA图像与真实CFP图像相结合，比所比较的FFA合成方法具有更高的诊断准确率。我们的研究弥合了非侵入性成像与FFA之间的差距，从而为增强眼科诊断和患者护理提供了有前景的前景，重点在于通过非侵入性操作减少对患者的伤害。我们的数据集和代码将公开发布，以支持该领域的进一步研究（https://github.com/whq-xxh/FFA-Synthesis）。
英文摘要：
Fundus imaging is a pivotal tool in ophthalmology, and different imaging modalities are characterized by their specific advantages. For example, Fundus Fluorescein Angiography (FFA) uniquely provides detailed insights into retinal vascular dynamics and pathology, surpassing Color Fundus Photographs (CFP) in detecting microvascular abnormalities and perfusion status. However, the conventional invasive FFA involves discomfort and risks due to fluorescein dye injection, and it is meaningful but challenging to synthesize FFA images from non-invasive CFP. Previous studies primarily focused on FFA synthesis in a single disease category. In this work, we explore FFA synthesis in multiple diseases by devising a Diffusion-guided generative adversarial network, which introduces an adaptive and dynamic diffusion forward process into the discriminator and adds a category-aware representation enhancer. Moreover, to facilitate this research, we collect the first multi-disease CFP and FFA paired dataset, named the Multi-disease Paired Ocular Synthesis (MPOS) dataset, with four different fundus diseases. Experimental results show that our FFA synthesis network can generate better FFA images compared to state-of-the-art methods. Furthermore, we introduce a paired-modal diagnostic network to validate the effectiveness of synthetic FFA images in the diagnosis of multiple fundus diseases, and the results show that our synthesized FFA images with the real CFP images have higher diagnosis accuracy than that of the compared FFA synthesizing methods. Our research bridges the gap between non-invasive imaging and FFA, thereby offering promising prospects to enhance ophthalmic diagnosis and patient care, with a focus on reducing harm to patients through non-invasive procedures. Our dataset and code will be released to support further research in this field (https://github.com/whq-xxh/FFA-Synthesis).
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