DECNet: A Dual-stream Edge Complementary Network for Retinal Vessel Segmentation

Xu, Weijin; Yang, Huihua; Zhang, Mingying; Pan, Xipeng; Liu, Wentao; Yan, Songlin

doi:10.1109/bibm52615.2021.9669751

Cited by 5 publications

(1 citation statement)

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“…Diabetic retinopathy, glaucoma, and age-related macular degeneration are major causes of blindness in the elderly [ 1 ]. In clinical practice, physicians diagnose these retinal diseases by analyzing the morphology of blood vessels, and based on the structure and location of vessels, they plan surgeries and guide interventions [ 2 , 3 ]. However, due to the limitations of imaging devices and the inherent characteristics of biological tissues, the initially acquired medical images often fail to provide an accurate representation of structural information.…”

Section: Introductionmentioning

confidence: 99%

FRD-Net: a full-resolution dilated convolution network for retinal vessel segmentation

Huang,

Shang,

2024

Biomed. Opt. Express

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Accurate and automated retinal vessel segmentation is essential for performing diagnosis and surgical planning of retinal diseases. However, conventional U-shaped networks often suffer from segmentation errors when dealing with fine and low-contrast blood vessels due to the loss of continuous resolution in the encoding stage and the inability to recover the lost information in the decoding stage. To address this issue, this paper introduces an effective full-resolution retinal vessel segmentation network, namely FRD-Net, which consists of two core components: the backbone network and the multi-scale feature fusion module (MFFM). The backbone network achieves horizontal and vertical expansion through the interaction mechanism of multi-resolution dilated convolutions while preserving the complete image resolution. In the backbone network, the effective application of dilated convolutions with varying dilation rates, coupled with the utilization of dilated residual modules for integrating multi-scale feature maps from adjacent stages, facilitates continuous learning of multi-scale features to enhance high-level contextual information. Moreover, MFFM further enhances segmentation by fusing deeper multi-scale features with the original image, facilitating edge detail recovery for accurate vessel segmentation. In tests on multiple classical datasets,compared to state-of-the-art segmentation algorithms, FRD-Net achieves superior performance and generalization with fewer model parameters.

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Section: Introductionmentioning

confidence: 99%