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.

scholarly journals Impact of Novel Image Preprocessing Techniques on Retinal Vessel Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2297
Author(s):  
Toufique A. Soomro ◽  
Ahmed Ali ◽  
Nisar Ahmed Jandan ◽  
Ahmed J. Afifi ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Retinal Disease ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Low Contrast ◽  
The Public ◽  
Retinal Blood Vessels ◽  
Retinal Vessel Segmentation ◽  
Automated Screening ◽  
Traditional Image

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.

scholarly journals Hybrid UNet Architecture based on Residual Learning of Fundus Images for Retinal Vessel Segmentation

2021 ◽  
Vol 2070 (1) ◽  
pp. 012104
Author(s):  
Sushma Nagdeote ◽  
Sapna Prabhu
Keyword(s):  
Blood Vessels ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Intensity Difference ◽  
Fundus Images ◽  
The Past ◽  
Retinal Blood Vessels ◽  
Retinal Fundus Image ◽  
Retinal Vessel Segmentation ◽  
Retinal Fundus

Abstract This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.

scholarly journals Automatic Retinal Vessel Segmentation Based on an Improved U-Net Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zihe Huang ◽  
Ying Fang ◽  
He Huang ◽  
Xiaomei Xu ◽  
Jiwei Wang ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Structural Changes ◽  
Area Under The Curve ◽  
Image Data ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Circulation System ◽  
Retinal Blood Vessels ◽  
Image Patches ◽  
Retinal Vessel Segmentation

Retinal blood vessels are the only deep microvessels in the blood circulation system that can be observed directly and noninvasively, providing us with a means of observing vascular pathologies. Cardiovascular and cerebrovascular diseases, such as glaucoma and diabetes, can cause structural changes in the retinal microvascular network. Therefore, the study of effective retinal vessel segmentation methods is of great significance for the early diagnosis of cardiovascular diseases and the vascular network’s quantitative results. This paper proposes an automatic retinal vessel segmentation method based on an improved U-Net network. Firstly, the image patches are rotated to amplify the image data, and then, the RGB fundus image is preprocessed by normalization. Secondly, after the improved U-Net model is constructed with 23 convolutional layers, 4 pooling layers, 4 upsampling layers, 2 dropout layers, and Squeeze and Excitation (SE) block, the extracted image patches are utilized for training the model. Finally, the fundus images are segmented through the trained model to achieve precise extraction of retinal blood vessels. According to experimental results, the accuracy of 0.9701, 0.9683, and 0.9698, sensitivity of 0.8011, 0.6329, and 0.7478, specificity of 0.9849, 0.9967, and 0.9895, F1-Score of 0.8099, 0.8049, and 0.8013, and area under the curve (AUC) of 0.8895, 0.8845, and 0.8686 were achieved on DRIVE, STARE, and HRF databases, respectively, which is better than most classical algorithms.

scholarly journals Retinal Vessel Analysis for Detection of Glaucoma

2021 ◽  
Vol 9 (VII) ◽  
pp. 3661-3667
Author(s):  
Chandana R
Keyword(s):  
Intraocular Pressure ◽  
Optic Nerve ◽  
Blood Vessels ◽  
Vision Loss ◽  
Retinal Vessel ◽  
Automated Analysis ◽  
Vessel Segmentation ◽  
Morphological Attributes ◽  
Retinal Blood Vessels ◽  
Retinal Vessel Segmentation

Glaucoma, a disease of the optic nerve is caused by the increase in the intraocular pressure of the eye and results in damage to the optic nerve and vision loss. The main characteristic of glaucoma is an elevated intraocular pressure (IOP) and also the blood vessels get narrower. Vessel segmentation is one of the main steps in retinal automated analysis tools. Retinal vessel segmentation and delineation of morphological attributes of retinal blood vessels are utilized for diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases. Since, the numbers of blood vessels are more in the glaucomatous eye , glaucoma is detected by means of ISNT ratio. The image processing operations are performed on glaucomatous and normal eyes. We have chosen ten images of each from the database and ISNT ratio is calculated to get the area of blood vessels in each of the four quadrants of the eye and hence glaucoma is detected.

scholarly journals MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction

2021 ◽  
Vol 12 (1) ◽  
pp. 403
Author(s):  
Lin Pan ◽  
Zhen Zhang ◽  
Shaohua Zheng ◽  
Liqin Huang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Automatic Segmentation ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Multitask Learning ◽  
Learning Network ◽  
Centerline Extraction ◽  
Retinal Vessel Segmentation ◽  
And Performance

Automatic segmentation and centerline extraction of blood vessels from retinal fundus images is an essential step to measure the state of retinal blood vessels and achieve the goal of auxiliary diagnosis. Combining the information of blood vessel segments and centerline can help improve the continuity of results and performance. However, previous studies have usually treated these two tasks as separate research topics. Therefore, we propose a novel multitask learning network (MSC-Net) for retinal vessel segmentation and centerline extraction. The network uses a multibranch design to combine information between two tasks. Channel and atrous spatial fusion block (CAS-FB) is designed to fuse and correct the features of different branches and different scales. The clDice loss function is also used to constrain the topological continuity of blood vessel segments and centerline. Experimental results on different fundus blood vessel datasets (DRIVE, STARE, and CHASE) show that our method can obtain better segmentation and centerline extraction results at different scales and has better topological continuity than state-of-the-art methods.

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