# Topology-Aware Retinal Artery-Vein Classification via Deep Vascular Connectivity Prediction
## 1 Introduction  
Discovering abnormal vasculature provides vital medical signs, since vascular changes are often a sign of the onset of symptoms. Thus, the timely recognition may allow preventive treatments and can reduce related risks. Retinal vessels, which are of interest in this paper, are especially known to be a biomarker for neurologic, cardiac, and systemic diseases, as well as ocular diseases [1]. In particular, the arteriolar-venular ratio (AVR), which is a ratio between the widths of arterioles and venules, is directly affected by diabetic retinopathy and cholesterol levels [2]. It is also known to be correlated with cognitive decline [3], stroke [4], and coronary heart disease [5]. Therefore, it is important to precisely assess such a clinical parameter for monitoring, diagnostic, and treatment purposes. To perform such a quantitative analysis in a retinal image, several tasks, such as optic disc detection [6], binary vessel segmentation [7, 8]--which just separates vessels from the background without considering their types--and artery-vein (AV) classification [9, 10, 11, 12]--where vessels are further classified into arteries and veins--should precede. Thereafter, vessel width measurement is conducted for separate arteries and veins in a determined concentric zone surrounding the optic disc for calculating the AVR [13]. For reliable and accurate measurement, the constituent tasks are traditionally conducted in a manual or semi-automatic manner, which is laborious and time-consuming. Over the years, there have been attempts to develop automatic methods that are confidently usable in clinical practice for retinal vessel analysis. In this paper, we present amethod for AV classification where the underlying vessel topology is estimated to better classify thin vessels. In the topology estimation, a whole vessel structure graph is separated into sub-trees branching out from the optic disc based on actual physical connectivity. We believe that observing the changes in thinner vessels can provide an even earlier biomarker, since those are more vulnerable to pathological changes than thick vessels. Although the current success of convolutional neural networks (CNNs) also shed light on AV classification [12], it still shows a weakness on thin vessels. Indistinct appearances, e.g., brightness characteristics, of thin vessels could be insufficient in differentiating between arteries and veins. We found that there is an accuracy gap between thick and thin vessels when a CNN-based pixelwise AV classification model is applied. In the proposed method, the AV classification is conducted on a tree basis to complement unreliable classification of thin vessels using the estimated topology (Figure 1). This presents less noisy results than the pixelwise classification, which are thus more adoptable in the following steps for computing the AVR. Several graph-theoretic approaches have been developed with similar objectives, which are to make use of the underlying vessel topology for improved AV classification [9; 10; 11]. In [9], different rule-based algorithms are applied for each node degree to classify the node type into connecting, bifurcation, crossing, and meeting points in a graph generated from the vessel segmentation. Afterward, starting from the edge farthest from the optic disc center, each edge is iteratively labeled based on the estimated type of the connected node. Meanwhile, [PERSON] et al. [10] use a global likelihood model to attain the most plausible label set given a semi-automatically extracted planar vessel graph. Recently, the concept of dominant set clustering is adopted and gradually applied twice for topology estimation and AV classification in [11]. Compared to the previous works, different heuristic rules for different node degrees [9], complex optimization schemes for the solution [10], and manual feature design for classification [9; 10; 11] are not required in the proposed method.