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 SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

2021 ◽  
Author(s):  
Chao Ma
Keyword(s):  
Supervised Learning ◽  
Learning Strategy ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
The Self ◽  
Supervised Training ◽  
Vascular Segmentation ◽  
Retinal Vessel Segmentation ◽  
Retinal Segmentation ◽  
First Time

We propose a SSL-Unet model for retinal vascular segmentation as well as two self-supervised training strategies. The strategy can help the self-supervised module to learn pseudo labels for improving the segmentation performance. Moreover, the fusion of both self-supervised and supervised paradigms is applied to retinal segmentation for the first time. Meanwhile, it can also be extended to any segmentation network.

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 SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

2021 ◽  
Author(s):  
Chao Ma
Keyword(s):  
Supervised Learning ◽  
Learning Strategy ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
The Self ◽  
Supervised Training ◽  
Vascular Segmentation ◽  
Retinal Vessel Segmentation ◽  
Retinal Segmentation ◽  
First Time

We propose a SSL-Unet model for retinal vascular segmentation as well as two self-supervised training strategies. The strategy can help the self-supervised module to learn pseudo labels for improving the segmentation performance. Moreover, the fusion of both self-supervised and supervised paradigms is applied to retinal segmentation for the first time. Meanwhile, it can also be extended to any segmentation network.

scholarly journals Robust Retinal Vessel Segmentation from a Data Augmentation Perspective

2021 ◽  
pp. 189-198
Author(s):  
Xu Sun ◽  
Huihui Fang ◽  
Yehui Yang ◽  
Dongwei Zhu ◽  
Lei Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Retinal Vessel Segmentation

scholarly journals Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1112 ◽  
Author(s):  
Yun Jiang ◽  
Hai Zhang ◽  
Ning Tan ◽  
Li Chen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Clinical Medicine ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Benchmark Tests ◽  
Fully Convolutional Neural Networks ◽  
Average Accuracy ◽  
Retinal Vessel Segmentation

Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the symmetrical and asymmetrical patterns between blood vessels are complicated, and the contrast between the vessel and the background is relatively low due to illumination and pathology. Robust vessel segmentation of the retinal image is essential for improving the diagnosis of diseases such as vein occlusions and diabetic retinopathy. Automated retinal vein segmentation remains a challenging task. In this paper, we proposed an automatic retinal vessel segmentation framework using deep fully convolutional neural networks (FCN), which integrate novel methods of data preprocessing, data augmentation, and full convolutional neural networks. It is an end-to-end framework that automatically and efficiently performs retinal vessel segmentation. The framework was evaluated on three publicly available standard datasets, achieving F1 score of 0.8321, 0.8531, and 0.8243, an average accuracy of 0.9706, 0.9777, and 0.9773, and average area under the Receiver Operating Characteristic (ROC) curve of 0.9880, 0.9923 and 0.9917 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. The experimental results show that our proposed framework achieves state-of-the-art vessel segmentation performance in all three benchmark tests.

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.

scholarly journals A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 811
Author(s):  
Dan Yang ◽  
Guoru Liu ◽  
Mengcheng Ren ◽  
Bin Xu ◽  
Jiao Wang
Keyword(s):  
Data Augmentation ◽  
Feature Fusion ◽  
Automatic Segmentation ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Feature Maps ◽  
Scale Feature ◽  
Multi Scale ◽  
Retinal Blood Vessels ◽  
Retinal Vessel Segmentation

Computer-aided automatic segmentation of retinal blood vessels plays an important role in the diagnosis of diseases such as diabetes, glaucoma, and macular degeneration. In this paper, we propose a multi-scale feature fusion retinal vessel segmentation model based on U-Net, named MSFFU-Net. The model introduces the inception structure into the multi-scale feature extraction encoder part, and the max-pooling index is applied during the upsampling process in the feature fusion decoder of an improved network. The skip layer connection is used to transfer each set of feature maps generated on the encoder path to the corresponding feature maps on the decoder path. Moreover, a cost-sensitive loss function based on the Dice coefficient and cross-entropy is designed. Four transformations—rotating, mirroring, shifting and cropping—are used as data augmentation strategies, and the CLAHE algorithm is applied to image preprocessing. The proposed framework is tested and trained on DRIVE and STARE, and sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under curve (AUC) are adopted as the evaluation metrics. Detailed comparisons with U-Net model, at last, it verifies the effectiveness and robustness of the proposed model. The Sen of 0.7762 and 0.7721, Spe of 0.9835 and 0.9885, Acc of 0.9694 and 0.9537 and AUC value of 0.9790 and 0.9680 were achieved on DRIVE and STARE databases, respectively. Results are also compared to other state-of-the-art methods, demonstrating that the performance of the proposed method is superior to that of other methods and showing its competitive results.

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.

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