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

Chen Ding,Runze Li,Lei Zhang,Youfa Chen,Zhouyi Zheng,Dushi Wen,Wei Wei,Yanning Zhang

doi:10.3390/app112411907

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.

Highlights

Retinal blood vessels are the only part of the systemic blood vessels that can be observed in a non-invasive way
In order to solve this problem, this paper proposes a multiple multi-scale neural network knowledge transfer and integration method for accurate pixel-level retinal blood vessel segmentation
The image blocks with a size of 23 × 23 were extracted from the DVIRE dataset, and the image blocks with a size of 31 × 31 were extracted from the CHASE dataset

Summary

Introduction

Retinal blood vessels are the only part of the systemic blood vessels that can be observed in a non-invasive way. According to above reasons, which lead to the difficulty of retinal blood vessel segmentation, the machine learning-based retinal vessel segmentation methods are urgently needed [6,7]. Among non-deep learning methods, it was the first proposed to use a Gaussian filter to segment the blood vessel image, which used the features of the blood vessel to solve the difficulties for segmentation, e.g., low contrast of the local blood vessel [8]. The non-deep learning-based retinal vessel segmentation methods often relied on empirical feature extraction and led to poor segmentation effect. For this reason, deep learning-based methods were proposed to be used for segmentation

Methods

Results

Discussion

Conclusion