Retinal blood vessel segmentation based on Densely Connected U-Net

Yinlin Cheng; Mengnan Ma; Liangjun Zhang; ChenJin Jin; Li Ma; Yi Zhou; Yinlin Cheng; Mengnan Ma; Liangjun Zhang; ChenJin Jin; Li Ma; Yi Zhou

doi:10.3934/mbe.2020175

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 3088-3108. doi: 10.3934/mbe.2020175

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Research article

Retinal blood vessel segmentation based on Densely Connected U-Net

1.
School of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510006, China
2.
Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510006, China
3.
Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510006, China
† These authors contributed equally to this work

Received: 30 December 2019 Accepted: 07 April 2020 Published: 15 April 2020

The segmentation of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper proposes a new architecture of the U-Net network for retinal blood vessel segmentation. Adding dense block to U-Net network makes each layer's input come from the all previous layer's output which improves the segmentation accuracy of small blood vessels. The effectiveness of the proposed method has been evaluated on two public datasets (DRIVE and CHASE_DB1). The obtained results (DRIVE: Acc = 0.9559, AUC = 0.9793, CHASE_DB1: Acc = 0.9488, AUC = 0.9785) demonstrate the better performance of the proposed method compared to the state-of-the-art methods. Also, the results show that our method achieves better results for the segmentation of small blood vessels and can be helpful to evaluate related ophthalmic diseases.
- retinal fundus image,
- U-Net,
- blood vessel segmentation,
- neural networks,
- dense block
Citation: Yinlin Cheng, Mengnan Ma, Liangjun Zhang, ChenJin Jin, Li Ma, Yi Zhou. Retinal blood vessel segmentation based on Densely Connected U-Net[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3088-3108. doi: 10.3934/mbe.2020175

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Abstract

The segmentation of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper proposes a new architecture of the U-Net network for retinal blood vessel segmentation. Adding dense block to U-Net network makes each layer's input come from the all previous layer's output which improves the segmentation accuracy of small blood vessels. The effectiveness of the proposed method has been evaluated on two public datasets (DRIVE and CHASE_DB1). The obtained results (DRIVE: Acc = 0.9559, AUC = 0.9793, CHASE_DB1: Acc = 0.9488, AUC = 0.9785) demonstrate the better performance of the proposed method compared to the state-of-the-art methods. Also, the results show that our method achieves better results for the segmentation of small blood vessels and can be helpful to evaluate related ophthalmic diseases.

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