Fundus Image Based Retinal Vessel Segmentation Utilizing a Fast and Accurate Fully Convolutional Network

2019 ◽  
pp. 112-120 ◽  
Author(s):  
Junyan Lyu ◽  
Pujin Cheng ◽  
Xiaoying Tang
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Fundus Image ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Retinal Vessel Segmentation

Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database

2013 ◽  
Vol 7 (4) ◽  
pp. 373-383 ◽  
Author(s):  
Jan Odstrcilik ◽  
Radim Kolar ◽  
Tomas Kubena ◽  
Pavel Cernosek ◽  
Attila Budai ◽  
...  
Keyword(s):  
High Resolution ◽  
Retinal Vessel ◽  
Image Database ◽  
Vessel Segmentation ◽  
Matched Filtering ◽  
Fundus Image ◽  
Retinal Vessel Segmentation

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 Improved retinal vessel segmentation using the enhanced pre-processing method for high resolution fundus images

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1222
Author(s):  
Aziah Ali ◽  
Aini Hussain ◽  
Wan Mimi Diyana Wan Zaki ◽  
Wan Haslina Wan Abdul Halim ◽  
Wan Noorshahida Mohd Isa ◽  
...  
Keyword(s):  
High Resolution ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Age Related Macular Degeneration ◽  
Qualitative Assessment ◽  
Fundus Images ◽  
Fundus Image ◽  
Age Related ◽  
Noisy Pixel ◽  
Retinal Vessel Segmentation

Background: By diagnosing using fundus images, ophthalmologists can possibly detect symptoms of retinal diseases such as diabetic retinopathy, age-related macular degeneration, and retinal detachment. A number of studies have also found some links between fundus image analysis data and other underlying systemic diseases such as cardiovascular diseases, including hypertension and kidney dysfunction. Now that imaging technology is advancing further, more fundus cameras are currently equipped with the capability to produce high resolution fundus images. One of the public databases for high-resolution fundus images called High-Resolution Fundus (HRF) is consistently used for validating vessel segmentation algorithms. However, it is noticed that the segmentation outputs from the HRF database normally include noisy pixels near the upper and lower edges of the image. In this study, we propose an enhanced method of pre-processing the images so that these noisy pixels can be eliminated, and thus the overall segmentation performance can be increased. Without eliminating the noisy pixels, the visual segmentation output shows a large number of false positive pixels near the top and bottom edges. Methods: The proposed method involves adding additional padding to the image before the segmentation procedure is applied. In this study, the Bar-Combination Of Shifted FIlter REsponses (B-COSFIRE) filter is used for retinal vessel segmentation. Results: Qualitative assessment of the segmentation results when using the proposed method showed improvement in terms of noisy pixel removal from near the edges. Quantitatively, the additional padding step improves all considered metrics for vessel segmentation, namely Sensitivity (73.76%), Specificity (97.53%), and Matthew’s Correlation Coefficient (MCC) value (71.57%) for the HRF database. Conclusions: Findings from this study indicate improvement in the overall segmentation performance when using the proposed double-padding method of pre-processing the fundus image prior to segmentation. In the future, more databases with various resolutions and modalities can be included for further validation.

scholarly journals HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257013
Author(s):  
Xiaolong Hu ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Deep Learning ◽  
Blood Vessels ◽  
Structural Information ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Convolutional Network ◽  
Retinal Blood Vessels ◽  
Retinal Vessel Segmentation ◽  
Ophthalmic Diseases ◽  
Sensitivity Specificity

The cardinal symptoms of some ophthalmic diseases observed through exceptional retinal blood vessels, such as retinal vein occlusion, diabetic retinopathy, etc. The advanced deep learning models used to obtain morphological and structural information of blood vessels automatically are conducive to the early treatment and initiative prevention of ophthalmic diseases. In our work, we propose a hierarchical dilation convolutional network (HDC-Net) to extract retinal vessels in a pixel-to-pixel manner. It utilizes the hierarchical dilation convolution (HDC) module to capture the fragile retinal blood vessels usually neglected by other methods. An improved residual dual efficient channel attention (RDECA) module can infer more delicate channel information to reinforce the discriminative capability of the model. The structured Dropblock can help our HDC-Net model to solve the network overfitting effectively. From a holistic perspective, the segmentation results obtained by HDC-Net are superior to other deep learning methods on three acknowledged datasets (DRIVE, CHASE-DB1, STARE), the sensitivity, specificity, accuracy, f1-score and AUC score are {0.8252, 0.9829, 0.9692, 0.8239, 0.9871}, {0.8227, 0.9853, 0.9745, 0.8113, 0.9884}, and {0.8369, 0.9866, 0.9751, 0.8385, 0.9913}, respectively. It surpasses most other advanced retinal vessel segmentation models. Qualitative and quantitative analysis demonstrates that HDC-Net can fulfill the task of retinal vessel segmentation efficiently and accurately.

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