Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed...

Area of Science:

• Ophthalmology
• Medical Imaging
• Computer Vision

Background:

• Accurate analysis of the retinal vascular tree is crucial for diagnosing ophthalmic and systemic diseases.
• Identifying intersections and bifurcations in retinal vessels is challenging but vital for understanding vascular morphology and network complexity.

Purpose of the Study:

• To develop an automated method for segmenting the vascular network in color fundus images.
• To accurately detect and classify vascular intersections and bifurcations using a novel neural network approach.

Main Methods:

• A directed graph search-based multi-attentive neural network was employed for vascular segmentation.
• Multi-dimensional attention mechanisms integrated local and global features for scale-adaptive target structure focus.
• Vascular topology and connectivity were represented using a directed graphical model.
• Local geometric information was utilized to decompose the vascular tree and classify feature points.

Main Results:

• The method achieved an F1-score of 0.863 on the DRIVE dataset and 0.764 on the IOSTAR dataset for detection points.
• Average accuracy for classification points reached 0.914 on DRIVE and 0.854 on IOSTAR.
• The proposed approach demonstrated superior performance compared to existing state-of-the-art methods in feature point detection and classification.

Conclusions:

• The novel directed graph search-based multi-attentive neural network effectively segments retinal vasculature and identifies key feature points.
• This method offers a significant improvement for automated retinal biomarker analysis, aiding in disease diagnosis.
• The approach shows strong potential for clinical applications in ophthalmology and systemic disease monitoring.