Abstract：
Accurate segmentation of retinal vessels in fundus images is of great importance for the diagnosis of numerous ocular diseases. However, due to the complex characteristics of fundus images, such as various lesions, image noise and complex background, the pixel features of some vessels have significant differences, which makes it easy for the segmentation networks to misjudge these vessels as noise, thus affecting the accuracy of the overall segmentation. Therefore, accurately segment retinal vessels in complex situations is still a great challenge. To address the problem, a partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation is proposed. The core idea of the proposed network is first to use the partial class activation mapping guided graph convolutional network to eliminate the differences of local vessels and generate feature maps with global consistency, and subsequently these feature maps are further refined by segmentation network U-Net to achieve better segmentation results. Specifically, a new neural network block, called EdgeConv, is stacked multiple layers to form a graph convolutional network to realize the transfer an update of information from local to global, so as gradually enhance the feature consistency of graph nodes. Simultaneously, in an effort to suppress the noise information that may be transferred in graph convolution and thus reduce adverse effects of noise on segmentation results, the partial class activation mapping is introduced. The partial class activation mapping can guide the information transmission between graph nodes and effectively activate vessel feature through classification labels, thereby improving the accuracy of segmentation. The performance of the proposed method is validated on four different fundus image datasets. Compared with existing state-of-the-art methods, the proposed method can improve the integrity of vessel to a certain extent when the pixel features of local vessels are significantly different, caused by objective factors such as inappropriate illumination and exudates. Moreover, the proposed method shows robustness when segmenting complex retinal vessels.
摘要：
眼底图像中视网膜血管的准确分割对于众多眼部疾病的诊断具有重要意义。然而,由于眼底图像的复杂特征，如各种病变，图像噪声和复杂的背景，一些血管的像素特征有显著差异，这使得分割网络很容易将这些血管误判为噪声，从而影响整体分割的准确性。因此,在复杂情况下准确分割视网膜血管仍然是一个很大的挑战。为了解决这个问题，提出了一种用于视网膜血管分割的部分类激活映射引导图卷积级联U-Net。提出的网络的核心思想是首先使用部分类激活映射引导图卷积网络来消除局部船只的差异，并生成具有全局一致性的特征图，随后通过分割网络U-Net进一步细化这些特征图，以达到更好的分割效果。具体来说,一个新的神经网络模块，叫EdgeConv,多层堆叠形成一个图卷积网络，实现信息从局部到全局的更新，从而逐步增强图节点的特征一致性。同时,为了抑制可能在图卷积中传输的噪声信息，从而减少噪声对分割结果的不利影响，介绍了部分类激活映射。部分类激活映射可以指导图节点之间的信息传递,通过分类标签有效激活血管特征,从而提高分割的准确性。在四个不同的眼底图像数据集上验证了所提出方法的性能。与现有的最先进的方法相比，当局部血管的像素特征存在显著差异时,该方法能在一定程度上提高血管的完整性,由不适当的照明和渗出物等客观因素引起的。此外，所提出的方法在分割复杂的视网膜血管时具有鲁棒性。
