Abstract
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.
Highlights
Image segmentation in retinal blood vessels has an important medical application value [1]
Motivated by the fact that wide activation, which focuses on the width of deep learning, propagates more low-level features with the same parameter complexity [25], in this study, we develop a retinal vessel segmentation method by deep residual learning based on wide activation
An overview of the proposed segmentation model is shown in Figure 1. e original retinal image is preprocessed and input to wide activation network (WA-Net), and the segmented image is output after network mapping. is framework has two main modules, denoted as wide activated module and like ASPP (LASPP) module, which will be introduced in the following parts
Summary
Image segmentation in retinal blood vessels has an important medical application value [1]. Computational Intelligence and Neuroscience used a structured output support vector machine to learn model parameters and perform retinal vessel segmentation For these traditional supervised methods, the final results predicted are greatly influenced by the features used for classification. Budak et al [24], in their recent work, showed that the weak and thin vessels are better segmented by the cascading CNN of lesser depth as long as feature utilization is improved For this reason, and motivated by the fact that wide activation, which focuses on the width of deep learning, propagates more low-level features with the same parameter complexity [25], in this study, we develop a retinal vessel segmentation method by deep residual learning based on wide activation. E remaining of this paper is organized as follows: Section 2 presents the proposed method, Section 3 provides implementation details, Section 4 analyzes the experimental results, and discussion and conclusion are drawn in Section 5 and Section 6, respectively
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