Multiple Multi-Scale Neural Networks Knowledge Transfer and Integration for Accurate Pixel-Level Retinal Blood Vessel Segmentation

Abstract

Retinal blood vessel segmentation plays an important role for analysis of retinal diseases, such as diabetic retinopathy and glaucoma. However, retinal blood vessel segmentation remains a challenging task due to the low contrast between some vessels and background, the different presenting conditions caused by uneven illumination and the artificial segmentation results are influenced by human experience, which seriously affects the classification accuracy. To address this problem, we propose a multiple multi-scale neural networks knowledge transfer and integration method in order to accurately segment for retinal blood vessel image. With the integration of multi-scale networks and multi-scale input patches, the blood vessel segmentation performance is obviously improved. In addition, applying knowledge transfer to the network training process, the pre-trained network reduces the number of network training iterations. The experimental results on the DRIVE dataset and the CHASE_DB1 dataset show the effectiveness of the method, whose average accuracy on the two datasets are 96.74% and 97.38%, respectively.

Keywords

• retinal blood vessel image
• convolutional neural networks
• multiple multi-scale neural networks
• knowledge transfer and integration
• pixel-level