State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China; School of Life Sciences, South China University of Technology, Guangzhou, 510006, Guangdong, China.
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
Comput Biol Med. 2023 Mar;155:106647. doi: 10.1016/j.compbiomed.2023.106647. Epub 2023 Feb 15.
Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification of intersection and bifurcation points is quite challenging but important for disentangling complex vascular network and tracking vessel morphology. In this paper, we present a novel directed graph search-based multi-attentive neural network approach to automatically segment the vascular network and separate intersections and bifurcations from color fundus images. Our approach uses multi-dimensional attention to adaptively integrate local features and their global dependencies while learning to focus on target structures at different scales to generate binary vascular maps. A directed graphical representation of the vascular network is constructed to represent the topology and spatial connectivity of the vascular structures. Using local geometric information including color difference, diameter, and angle, the complex vascular tree is decomposed into multiple sub-trees to finally classify and label vascular feature points. The proposed method has been tested on the DRIVE dataset and the IOSTAR dataset containing 40 images and 30 images, respectively, with 0.863 and 0.764 F-score of detection points and average accuracy of 0.914 and 0.854 for classification points. These results demonstrate the superiority of our proposed method outperforming state-of-the-art methods in feature point detection and classification.
血管树的分析是自动诊断与眼科和全身疾病相关的视网膜生物标志物的基本前提，其中准确识别交点和分叉点对于理清复杂的血管网络和跟踪血管形态非常具有挑战性但又很重要。在本文中，我们提出了一种新颖的基于有向图搜索的多注意神经网 络方法，用于自动分割血管网络并从彩色眼底图像中分离交点和分叉。我们的方法使用多维注意力来自适应地整合局部特征及其全局依赖性，同时学习关注不同尺度的目标结构，以生成二值血管图。构建血管网络的有向图形表示来表示血管结构的拓扑和空间连接性。利用包括色差、直径和角度在内的局部几何信息，将复杂的血管树分解为多个子树，最终对血管特征点进行分类和标记。所提出的方法已经在包含 40 张图像的 DRIVE 数据集和包含 30 张图像的 IOSTAR 数据集上进行了测试，检测点的 F1 分数分别为 0.863 和 0.764，分类点的平均准确率分别为 0.914 和 0.854。这些结果表明，我们提出的方法在特征点检测和分类方面优于最先进的方法。
