SRV-GAN: A generative adversarial network for segmenting retinal vessels

Chen Yue; Mingquan Ye; Peipei Wang; Daobin Huang; Xiaojie Lu; Chen Yue; Mingquan Ye; Peipei Wang; Daobin Huang; Xiaojie Lu

doi:10.3934/mbe.2022464

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 10: 9948-9965. doi: 10.3934/mbe.2022464

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Research article

SRV-GAN: A generative adversarial network for segmenting retinal vessels

1.
School of Medical Information, Wannan Medical College, Wuhu 241002, China
2.
Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China

Academic Editor: Yang Kuang

Received: 18 March 2022 Revised: 23 June 2022 Accepted: 27 June 2022 Published: 12 July 2022

In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model for retinal blood vessel segmentation. Generative adversarial networks (GANs) have been used for image semantic segmentation and show good performance. So, this paper proposes an improved GAN. Based on R2U-Net, the generator adds an attention mechanism, channel and spatial attention, which can reduce the loss of information and extract more effective features. We use dense connection modules in the discriminator. The dense connection module has the characteristics of alleviating gradient disappearance and realizing feature reuse. After a certain amount of iterative training, the generated prediction map and label map can be distinguished. Based on the loss function in the traditional GAN, we introduce the mean squared error. By using this loss, we ensure that the synthetic images contain more realistic blood vessel structures. The values of area under the curve (AUC) in the retinal blood vessel pixel segmentation of the three public data sets DRIVE, CHASE-DB1 and STARE of the proposed method are 0.9869, 0.9894 and 0.9885, respectively. The indicators of this experiment have improved compared to previous methods.
- deep learning,
- retinal image segmentation,
- generative adversarial networks,
- attention,
- loss functions
Citation: Chen Yue, Mingquan Ye, Peipei Wang, Daobin Huang, Xiaojie Lu. SRV-GAN: A generative adversarial network for segmenting retinal vessels[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 9948-9965. doi: 10.3934/mbe.2022464

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Abstract

In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model for retinal blood vessel segmentation. Generative adversarial networks (GANs) have been used for image semantic segmentation and show good performance. So, this paper proposes an improved GAN. Based on R2U-Net, the generator adds an attention mechanism, channel and spatial attention, which can reduce the loss of information and extract more effective features. We use dense connection modules in the discriminator. The dense connection module has the characteristics of alleviating gradient disappearance and realizing feature reuse. After a certain amount of iterative training, the generated prediction map and label map can be distinguished. Based on the loss function in the traditional GAN, we introduce the mean squared error. By using this loss, we ensure that the synthetic images contain more realistic blood vessel structures. The values of area under the curve (AUC) in the retinal blood vessel pixel segmentation of the three public data sets DRIVE, CHASE-DB1 and STARE of the proposed method are 0.9869, 0.9894 and 0.9885, respectively. The indicators of this experiment have improved compared to previous methods.

References

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