Retinal vascular junction detection and classification via deep neural networks

Background and Objectives: The retinal fundus contains intricate vascular trees, some of which are mutually intersected and overlapped. The intersection and overlapping of retinal vessels represent vascular junctions (i.e. bifurcation and crossover) in 2D retinal images. These junctions are important for analyzing vascular diseases and tracking the morphology of vessels. In this paper, we propose a two-stage pipeline to detect and classify the junction points. Methods: In the detection stage, a RCNN-based Junction Proposal Network is utilized to search the potential bifurcation and crossover locations directly on color retinal images, which is followed by a Junction Refinement Network to eliminate the false detections. In the classification stage, the detected junction points are identified as crossover or bifurcation using the proposed Junction Classification Network that shares the same model structure with the refinement network. Results: Our approach achieves 70% and 60% F1-score on DRIVE and IOSTAR dataset respectively which outperform the state-of-the-art methods by 4.5% and 1.7%, with a high and balanced precision and recall values. Conclusions: This paper proposes a new junction detection and classification method which performs directly on color retinal images without any vessel segmentation nor skeleton preprocessing. The superior performance demonstrates that the effectiveness of our approach.