Development and Validation of Artery-Vein Ratio Measurement Based on Deep Learning Architectures

This paper presents an automated measurement of retinal artery and vein blood vessels using the state-of-the-art deep learning architectures. The measurement of the artery-vein ratio plays a vital role in predicting intracranial pressure (ICP) in traumatic patients. In the proposed method, the artery-vein and optic cup-to-disc (OCD) information are extracted from the retinal fundus imaging using deep CNN (D-CNN). The process involves the preprocessing of the retinal fundus image to highlight the vessels information and OCD more clearly. Further, the feature extraction of the vessels and OCD is performed using the base architecture of D-CNN. The extracted vessels and OCD determined the artery-vein ratio. The performance of the segmented artery-vein and OCD is evaluated and analyzed. Validation of measured artery-vein and OCD has been done by comparing these values with the ground truth values. The accuracy of the segmented artery-vein is determined to be 95.21 for the HRF dataset and the segmented optic cup and disc were found to be 94.70 and 92.36, respectively for the Drishti dataset. The extracted feature of artery-vein and OCD determines the artery-vein ratio using the connected component analysis. The algorithm-generated measured value is compared with the manually generated value by the two observer for the artery-vein ratio. The average error for the INSPIRE-AV dataset on total 40 images is found to be 0.15.