论文标题
黄斑的高度图重建在颜色眼底图像上使用条件生成对抗网络
Heightmap Reconstruction of Macula on Color Fundus Images Using Conditional Generative Adversarial Networks
论文作者
论文摘要
为了筛选，眼视网膜的3D形状通常提供结构信息，并可以帮助眼科医生诊断疾病。但是，由于其2D性质，这是视网膜诊断最常见的筛查方式之一的眼底图像缺乏此信息。因此，在这项工作中，我们试图推断出此3D信息或更具体地说的高度。最近的方法使用了阴影信息来重建高度，但是它们的输出不准确，因为所使用的信息还不够。此外，其他方法取决于在实践中无法使用的多个眼睛的可用性。在本文中，以条件生成对抗网络（CGAN）和深入监督网络的成功进行，我们为生成器提出了一种新型的体系结构，该架构以一系列步骤来增强细节。我们数据集上的比较表明，所提出的方法优于图像翻译和医疗图像翻译中的所有最新方法。此外，临床研究还表明，所提出的方法可以为眼科医生提供其他信息进行诊断。
For screening, 3D shape of the eye retina often provides structural information and can assist ophthalmologists to diagnose diseases. However, fundus images which are one the most common screening modalities for retina diagnosis lack this information due to their 2D nature. Hence, in this work, we try to infer about this 3D information or more specifically its heights. Recent approaches have used shading information for reconstructing the heights but their output is not accurate since the utilized information is not sufficient. Additionally, other methods were dependent on the availability of more than one image of the eye which is not available in practice. In this paper, motivated by the success of Conditional Generative Adversarial Networks(cGANs) and deeply supervised networks, we propose a novel architecture for the generator which enhances the details in a sequence of steps. Comparisons on our dataset illustrate that the proposed method outperforms all of the state-of-the-art methods in image translation and medical image translation on this particular task. Additionally, clinical studies also indicate that the proposed method can provide additional information for ophthalmologists for diagnosis.
