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

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.

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Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

Computational Intelligence and Neuroscience ◽

10.1155/2020/8822407 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuliang Ma ◽

Xue Li ◽

Xiaopeng Duan ◽

Yun Peng ◽

Yingchun Zhang

Keyword(s):

Blood Vessel ◽

Blood Vessels ◽

Area Under The Curve ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Superior Performance ◽

Retinal Blood Vessel ◽

Low Contrast ◽

Blood Vessel Segmentation ◽

Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

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