However, thin vessels cannot be segmented accurately. Recently, deep neural networks have boosted the segmentation performance of retinal vessel segmentation ( 10, 12) by a large margin compared with traditional methods ( 13, 14). To improve efficiency and reliability and reduce the workload of doctors, the clinical practice puts forward high requirements for automatic segmentation ( 9).
Conventionally, manual segmentation is laborious and time-consuming, and suffers subjectivity among experts. Increased vascular curvature or stenosis can be found in patients with hypertension ( 8). For example, hypertensive retinopathy is a retinal disease, which is caused by hypertension. Retinal vessel segmentation is one of the cornerstones to access those characteristics, particularly for automatic retinal image analysis ( 6, 7). Meanwhile, those characteristics are important biomarkers for numerous systemic diseases, including hypertension ( 4) and cardiovascular diseases ( 5). 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). In addition, our method is more efficient than existing methods with a large reduction in computational cost. 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.
We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. 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. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. 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. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery.