A lightweight dual-path cascaded network for vessel segmentation in fundus image

Yanxia Sun; Xiang Li; Yuechang Liu; Zhongzheng Yuan; Jinke Wang; Changfa Shi; Yanxia Sun; Xiang Li; Yuechang Liu; Zhongzheng Yuan; Jinke Wang; Changfa Shi

doi:10.3934/mbe.2023479

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10790-10814. doi: 10.3934/mbe.2023479

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A lightweight dual-path cascaded network for vessel segmentation in fundus image

1.
Department of Software Engineering, Harbin University of Science and Technology, Rongcheng 264300, China
2.
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
3.
Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, Changsha 410205, China

Academic Editor: Yudong Zhang

Received: 25 February 2023 Revised: 01 April 2023 Accepted: 10 April 2023 Published: 18 April 2023

Automatic and fast segmentation of retinal vessels in fundus images is a prerequisite in clinical ophthalmic diseases; however, the high model complexity and low segmentation accuracy still limit its application. This paper proposes a lightweight dual-path cascaded network (LDPC-Net) for automatic and fast vessel segmentation. We designed a dual-path cascaded network via two U-shaped structures. Firstly, we employed a structured discarding (SD) convolution module to alleviate the over-fitting problem in both codec parts. Secondly, we introduced the depthwise separable convolution (DSC) technique to reduce the parameter amount of the model. Thirdly, a residual atrous spatial pyramid pooling (ResASPP) model is constructed in the connection layer to aggregate multi-scale information effectively. Finally, we performed comparative experiments on three public datasets. Experimental results show that the proposed method achieved superior performance on the accuracy, connectivity, and parameter quantity, thus proving that it can be a promising lightweight assisted tool for ophthalmic diseases.
- segmentation,
- retinal vessels,
- fundus image,
- lightweight,
- cascade network
Citation: Yanxia Sun, Xiang Li, Yuechang Liu, Zhongzheng Yuan, Jinke Wang, Changfa Shi. A lightweight dual-path cascaded network for vessel segmentation in fundus image[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10790-10814. doi: 10.3934/mbe.2023479

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Abstract

Automatic and fast segmentation of retinal vessels in fundus images is a prerequisite in clinical ophthalmic diseases; however, the high model complexity and low segmentation accuracy still limit its application. This paper proposes a lightweight dual-path cascaded network (LDPC-Net) for automatic and fast vessel segmentation. We designed a dual-path cascaded network via two U-shaped structures. Firstly, we employed a structured discarding (SD) convolution module to alleviate the over-fitting problem in both codec parts. Secondly, we introduced the depthwise separable convolution (DSC) technique to reduce the parameter amount of the model. Thirdly, a residual atrous spatial pyramid pooling (ResASPP) model is constructed in the connection layer to aggregate multi-scale information effectively. Finally, we performed comparative experiments on three public datasets. Experimental results show that the proposed method achieved superior performance on the accuracy, connectivity, and parameter quantity, thus proving that it can be a promising lightweight assisted tool for ophthalmic diseases.

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