Retinal Vessel Segmentation Based on W-Net Conditional Generative Adversarial Nets

Accurate extraction of retinal vessels is one important factor to computer-aided diagnosis for ophthalmologic diseases. Due to the low sensitivity and insufficient segmentation of tiny blood vessels within the existing segmentation algorithms, a novel retinal vessel segmentation algorithm is proposed, and its basis is on conditional generative adversarial nets, using W-net as generator. More specifically, firstly, the U-net is expanded to W-net through the skip connection, as the U-net is beneficial to the microvascular information transmission in the skip connection layer, then the network convergence is accelerated and the parameter utilization is improved. Secondly, the standard convolutions are replaced by the depth-wise separable convolutions, thus expanding the network and reducing the number of the parameters. Thirdly, the residual blocks are employed to mitigate the gradient disappearance and the explosion. Fourthly, during the proposed algorithm, each skip connection follows Squeeze-and-Excitation blocks so that the shallow features and deep features can be effectively fused through learning the interdependence of feature channel. Generally, the loss function of the conditional generative adversarial nets is modified to make the overall segmentation performance be optimal, while having strong global penalty ability in the whole game learning model. Finally, one experiment is carried out on the DRIVE dataset with image enhancement and data expansion. From the experiment results, the segmentation sensitivity reaches 87.18%, further the specificity, accuracy and AUC are 98.19%, 96.95% and 98.42% respectively, which show the overall performance and sensitivity are better than the existing algorithms.

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Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.786425 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shuang Xu ◽

Zhiqiang Chen ◽

Weiyi Cao ◽

Feng Zhang ◽

Bo Tao

Keyword(s):

Neural Network ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Segmentation Algorithm ◽

Convolution Neural Network ◽

Superior Performance ◽

Retinal Vessels ◽

Fundus Images ◽

Data Set ◽

Retinal Vessel Segmentation

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.

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

Journal of Healthcare Engineering ◽

10.1155/2022/5188362 ◽

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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Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Journal of Clinical Medicine ◽

10.3390/jcm9041018 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1018 ◽

Cited By ~ 4

Author(s):

Md. Mohaimenul Islam ◽

Tahmina Nasrin Poly ◽

Bruno Andreas Walther ◽

Hsuan Chia Yang ◽

Yu-Chuan (Jack) Li

Keyword(s):

Systematic Review ◽

Deep Learning ◽

High Performance ◽

Meta Analysis ◽

Retinal Disease ◽

High Sensitivity ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Retinal Vessels ◽

Retinal Vessel Segmentation

Background and Objective: Accurate retinal vessel segmentation is often considered to be a reliable biomarker of diagnosis and screening of various diseases, including cardiovascular diseases, diabetic, and ophthalmologic diseases. Recently, deep learning (DL) algorithms have demonstrated high performance in segmenting retinal images that may enable fast and lifesaving diagnoses. To our knowledge, there is no systematic review of the current work in this research area. Therefore, we performed a systematic review with a meta-analysis of relevant studies to quantify the performance of the DL algorithms in retinal vessel segmentation. Methods: A systematic search on EMBASE, PubMed, Google Scholar, Scopus, and Web of Science was conducted for studies that were published between 1 January 2000 and 15 January 2020. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) procedure. The DL-based study design was mandatory for a study’s inclusion. Two authors independently screened all titles and abstracts against predefined inclusion and exclusion criteria. We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for assessing the risk of bias and applicability. Results: Thirty-one studies were included in the systematic review; however, only 23 studies met the inclusion criteria for the meta-analysis. DL showed high performance for four publicly available databases, achieving an average area under the ROC of 0.96, 0.97, 0.96, and 0.94 on the DRIVE, STARE, CHASE_DB1, and HRF databases, respectively. The pooled sensitivity for the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.77, 0.79, 0.78, and 0.81, respectively. Moreover, the pooled specificity of the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.97, 0.97, 0.97, and 0.92, respectively. Conclusion: The findings of our study showed the DL algorithms had high sensitivity and specificity for segmenting the retinal vessels from digital fundus images. The future role of DL algorithms in retinal vessel segmentation is promising, especially for those countries with limited access to healthcare. More compressive studies and global efforts are mandatory for evaluating the cost-effectiveness of DL-based tools for retinal disease screening worldwide.

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Application of Markov Random Field in the Retinal Vessel Segmentation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.696.114 ◽

2014 ◽

Vol 696 ◽

pp. 114-118 ◽

Cited By ~ 1

Author(s):

Wen Long Yin ◽

Hong Song Li ◽

Hao Ran Zhang ◽

Shu Ting Zhao

Keyword(s):

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Clustering Algorithm ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Model Parameters ◽

Retinal Vessels ◽

Markov Random ◽

Retinal Vessel Segmentation

Some diseases, particularly cardiovascular disease, will change the shape and structure of retinal vessels. Observation and detection of retinal vessels play an important role in the diagnosis of diseases. Traditional diagnosis of the retinal vessels that ophthalmologist perform under artificial visual attending. Image segmentation based on Markov random field is a method based on statistical theory, which takes into account the correlation between the local pixels, uses the prior knowledge effectively, has fewer model parameters and is easy to be combined with other methods etc., so this method has been widely researched and applied in the field of image segmentation. This paper which mainly studied the Markov random field is how to specific apply to image segmentation, and the iterated conditional mode and the traditional segmentation (clustering) algorithm segmented and compared in the medical retinal vessel image. The method of MRF can effectively restrain the noise in the vessel segmentation.

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Retinal Vessel Segmentation Through Circular Average Filtering and Thresholding for Speedy Computer-Aided Diagnosis Based Diabetic Retinopathy Detection System

Advanced Science Engineering and Medicine ◽

10.1166/asem.2018.2283 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1191-1195

Author(s):

Kalyan Acharjya ◽

Sudhir Kr. Sharma ◽

Jnanendra Upadhyay ◽

Shiksha Pandita

Keyword(s):

Diabetic Retinopathy ◽

Detection System ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Retinal Vessel Segmentation ◽

Aided Diagnosis

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Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation

Sensors ◽

10.3390/s21186177 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6177

Author(s):

Yun Jiang ◽

Huixia Yao ◽

Shengxin Tao ◽

Jing Liang

Keyword(s):

Retinal Vessel ◽

Vessel Segmentation ◽

Experimental Results ◽

Retinal Vessels ◽

Bilinear Interpolation ◽

Feature Maps ◽

Level Information ◽

Retinal Vessel Segmentation ◽

Flow Of Information ◽

F Measure

Segmentation of retinal vessels is a critical step for the diagnosis of some fundus diseases. Methods: To further enhance the performance of vessel segmentation, we propose a method based on a gated skip-connection network with adaptive upsampling (GSAU-Net). In GSAU-Net, a novel skip-connection with gating is first utilized in the extension path, which facilitates the flow of information from the encoder to the decoder. Specifically, we used the gated skip-connection between the encoder and decoder to gate the lower-level information from the encoder. In the decoding phase, we used an adaptive upsampling to replace the bilinear interpolation, which recovers feature maps from the decoder to obtain the pixelwise prediction. Finally, we validated our method on the DRIVE, CHASE, and STARE datasets. Results: The experimental results showed that our proposed method outperformed some existing methods, such as DeepVessel, AG-Net, and IterNet, in terms of accuracy, F-measure, and AUCROC. The proposed method achieved a vessel segmentation F-measure of 83.13%, 81.40%, and 84.84% on the DRIVE, CHASE, and STARE datasets, respectively.

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