scholarly journals SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jinke Wang ◽  
Xiang Li ◽  
Peiqing Lv ◽  
Changfa Shi
Keyword(s):  
Retinal Image ◽  
Area Under The Curve ◽  
Super Resolution ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Retinal Vessel Segmentation ◽  
Block Based ◽  
Public Datasets

Background and Objective. Accurate segmentation of retinal vessels is considered as an important prerequisite for computer-aided diagnosis of ophthalmic diseases, diabetes, glaucoma, and other diseases. Although current learning-based methods have greatly improved the performance of retinal vessel segmentation, the accuracy could not meet the requirements of clinical assistance yet. Methods. A new SERR-U-Net framework for retinal vessel segmentation is proposed, which leverages technologies including Squeeze-and-Excitation (SE), residual module, and recurrent block. First, the convolution layers of encoder and decoder are modified on the basis of U-Net, and the recurrent block is used to increase the network depth. Second, the residual module is utilized to alleviate the vanishing gradient problem. Finally, to derive more specific vascular features, we employed the SE structure to introduce attention mechanism into the U-shaped network. In addition, enhanced super-resolution generative adversarial networks (ESRGANs) are also deployed to remove the noise of retinal image. Results. The effectiveness of this method was tested on two public datasets, DRIVE and STARE. In the experiment of DRIVE dataset, the accuracy and AUC (area under the curve) of our method were 0.9552 and 0.9784, respectively, and for SATRE dataset, 0.9796 and 0.9859 were achieved, respectively, which proved a high accuracy and promising prospect on clinical assistance. Conclusion. An improved U-Net network combining SE, ResNet, and recurrent technologies is developed for automatic vessel segmentation from retinal image. This new model enables an improvement on the accuracy compared to learning-based methods, and its robustness in circumvent challenging cases such as small blood vessels and intersection of vessels is also well demonstrated and validated.

scholarly journals Retinal Vessel Segmentation Combined With Generative Adversarial Networks and Dense U-Net

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 194551-194560
Author(s):  
Xiaoyu Guo ◽  
Cheng Chen ◽  
Yuanyuan Lu ◽  
Ke Meng ◽  
Hongyu Chen ◽  
...  
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Retinal Vessel Segmentation

scholarly journals Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 168 ◽  
Author(s):  
Chang Wang ◽  
Zongya Zhao ◽  
Qiongqiong Ren ◽  
Yongtao Xu ◽  
Yi Yu
Keyword(s):  
Data Augmentation ◽  
Learning Strategy ◽  
Retinal Vessel ◽  
Semantic Segmentation ◽  
Training Model ◽  
Vessel Segmentation ◽  
Evaluation Metrics ◽  
Segmentation Result ◽  
Retinal Vessel Segmentation ◽  
Public Datasets

Various retinal vessel segmentation methods based on convolutional neural networks were proposed recently, and Dense U-net as a new semantic segmentation network was successfully applied to scene segmentation. Retinal vessel is tiny, and the features of retinal vessel can be learned effectively by the patch-based learning strategy. In this study, we proposed a new retinal vessel segmentation framework based on Dense U-net and the patch-based learning strategy. In the process of training, training patches were obtained by random extraction strategy, Dense U-net was adopted as a training network, and random transformation was used as a data augmentation strategy. In the process of testing, test images were divided into image patches, test patches were predicted by training model, and the segmentation result can be reconstructed by overlapping-patches sequential reconstruction strategy. This proposed method was applied to public datasets DRIVE and STARE, and retinal vessel segmentation was performed. Sensitivity (Se), specificity (Sp), accuracy (Acc), and area under each curve (AUC) were adopted as evaluation metrics to verify the effectiveness of proposed method. Compared with state-of-the-art methods including the unsupervised, supervised, and convolutional neural network (CNN) methods, the result demonstrated that our approach is competitive in these evaluation metrics. This method can obtain a better segmentation result than specialists, and has clinical application value.

scholarly journals Deep Att-ResGAN: A Retinal Vessel Segmentation Network for Color Fundus Images

2021 ◽  
Vol 38 (5) ◽  
pp. 1309-1317
Author(s):  
Jie Zhao ◽  
Qianjin Feng
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Original Image ◽  
Generative Adversarial Network ◽  
Low Contrast ◽  
Adversarial Network ◽  
Vessel Structure ◽  
Retinal Vessel Segmentation ◽  
Vessel Extraction ◽  
Public Datasets

Retinal vessel segmentation plays a significant role in the diagnosis and treatment of ophthalmological diseases. Recent studies have proved that deep learning can effectively segment the retinal vessel structure. However, the existing methods have difficulty in segmenting thin vessels, especially when the original image contains lesions. Based on generative adversarial network (GAN), this paper proposes a deep network with residual module and attention module (Deep Att-ResGAN). The network consists of four identical subnetworks. The output of each subnetwork is imported to the next subnetwork as contextual features that guide the segmentation. Firstly, the problems of the original image, namely, low contrast, uneven illumination, and data insufficiency, were solved through image enhancement and preprocessing. Next, an improved U-Net was adopted to serve as the generator, which stacks the residual and attention modules. These modules optimize the weight of the generator, and enhance the generalizability of the network. Further, the segmentation was refined iteratively by the discriminator, which contributes to the performance of vessel segmentation. Finally, comparative experiments were carried out on two public datasets: Digital Retinal Images for Vessel Extraction (DRIVE) and Structured Analysis of the Retina (STARE). The experimental results show that Deep Att-ResGAN outperformed the equivalent models like U-Net and GAN in most metrics. Our network achieved accuracy of 0.9565 and F1 of 0.829 on DRIVE, and accuracy of 0.9690 and F1 of 0.841 on STARE.

scholarly journals Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Congjun Liu ◽  
Penghui Gu ◽  
Zhiyong Xiao
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Good Effect ◽  
Retinal Vessels ◽  
Convolutional Network ◽  
Low Contrast ◽  
Retinal Vessel Segmentation ◽  
Feature Pyramid ◽  
Detection And Diagnosis ◽  
Public Datasets

Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.

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