Abstract
Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net.
Highlights
The subtle changes in the retinal vascular, including vessel width, tortuosity, and branching features, indicate mass eye-related diseases, such as diabetic retinopathy (1), glaucoma (2), and macular degeneration (3)
This work makes the following contributions: 1) We discovered that thin vessels suffer a big miss in the segmentation results of existing methods; 2) We proposed Pyramid-Net for retinal vessel segmentation in which intra-layer pyramid-scale aggregation blocks (IPABs) aggregate features at the higher, current, and lower scales to fuse coarse-to-fine context information in each layer; 3) We further propose three enhancements: pyramid input enhancement, deep pyramid supervision, and pyramid skip connections to boost the performance; 4) We conducted comprehensive experiments on three public vessel image datasets (DRIVE, STARE, and CHASE-DB1), and our method achieves the state-of-the-art performance on three datasets
We compared our Pyramid-Net with existing state-of-the-art works on three vessel image segmentation datasets (DRIVE, CHASE-DB1, and STARE)
Summary
The subtle changes in the retinal vascular, including vessel width, tortuosity, and branching features, indicate mass eye-related diseases, such as diabetic retinopathy (1), glaucoma (2), and macular degeneration (3). Those characteristics are important biomarkers for numerous systemic diseases, including hypertension (4) and cardiovascular diseases (5). Pyramid-Net for Retinal Image Segmentation vessel segmentation is one of the cornerstones to access those characteristics, for automatic retinal image analysis (6, 7). Manual segmentation is laborious and timeconsuming, and suffers subjectivity among experts. To improve efficiency and reliability and reduce the workload of doctors, the clinical practice puts forward high requirements for automatic segmentation (9)
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