Abstract
Fundus photography, in combination with the ultra-wide-angle fundus (UWF) techniques, becomes an indispensable diagnostic tool in clinical settings by offering a more comprehensive view of the retina. Nonetheless, UWF fluorescein angiography (UWF-FA) necessitates the administration of a fluorescent dye via injection into the patient's hand or elbow unlike UWF scanning laser ophthalmoscopy (UWF-SLO). To mitigate potential adverse effects associated with injections, researchers have proposed the development of cross-modality medical image generation algorithms capable of converting UWF-SLO images into their UWF-FA counterparts. Current image generation techniques applied to fundus photography encounter difficulties in producing high-resolution retinal images, particularly in capturing minute vascular lesions. To address these issues, we introduce a novel conditional generative adversarial network (UWAFA-GAN) to synthesize UWF-FA from UWF-SLO. This approach employs multi-scale generators and an attention transmit module to efficiently extract both global structures and local lesions. Additionally, to counteract the image blurriness issue that arises from training with misaligned data, a registration module is integrated within this framework. Our method performs non-trivially on inception scores and details generation. Clinical user studies further indicate that the UWF-FA images generated by UWAFA-GAN are clinically comparable to authentic images in terms of diagnostic reliability. Empirical evaluations on our proprietary UWF image datasets elucidate that UWAFA-GAN outperforms extant methodologies. The code is accessible at this https URL.
Abstract (translated)
fundus摄影与超广角 fundus（UWF）技术相结合，在临床实践中成为一项不可或缺的诊断工具，因为它能提供对视网膜更全面的观察。然而，UWF 荧光血管造影（UWF-FA）需要通过注射荧光染料到患者手中或肘部来实施，而 UWF 扫描激光视网膜检查（UWF-SLO）不需要这样做。为了减轻注射可能带来的不良反应，研究人员提出了开发能够将 UWF-SLO 图像转换为 UWF-FA 图像的跨模态医疗图像生成算法。目前应用于 fundus 摄影的图像生成技术在生成高分辨率视网膜图像方面遇到困难，特别是在捕捉细微血管病变方面。为了应对这些问题，我们引入了一种名为 UWAFA-GAN 的条件生成对抗网络（GAN）用于从 UWF-SLO 合成 UWF-FA。这种方法采用多尺度生成器和关注传输模块来有效地提取全局结构和局部病变。此外，为了对抗训练数据不对齐导致的图像模糊问题，我们在该框架中引入了注册模块。我们的方法在 inception 分数和详细信息生成方面非同寻常。通过对我们专有 UWF 图像数据集的临床用户研究，证实了 UWAFA-GAN 生成的 UWF-FA 图像在诊断可靠性方面与真实图像相当。我们专有 UWF 图像数据集的实证评估证实了 UWAFA-GAN 优于现有方法。代码可在此链接访问：https://www.example.com/
URL
https://arxiv.org/abs/2405.00542
