Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

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Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

10.36227/techrxiv.16653238.v1 ◽

2021 ◽

Author(s):

Sanjeewani NA ◽

arun kumar yadav ◽

Mohd Akbar ◽

mohit kumar ◽

Divakar Yadav

Keyword(s):

Deep Learning ◽

Blood Vessel ◽

Blood Vessels ◽

Automatic Segmentation ◽

Vessel Segmentation ◽

Great Success ◽

Retinal Blood Vessel ◽

Data Set ◽

Blood Vessel Segmentation ◽

Retinal Blood Vessels

<div>Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.</div>

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Multiple Multi-Scale Neural Networks Knowledge Transfer and Integration for Accurate Pixel-Level Retinal Blood Vessel Segmentation

Applied Sciences ◽

10.3390/app112411907 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11907

Author(s):

Chen Ding ◽

Runze Li ◽

Zhouyi Zheng ◽

Youfa Chen ◽

Dushi Wen ◽

...

Keyword(s):

Neural Networks ◽

Knowledge Transfer ◽

Blood Vessel ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Low Contrast ◽

Blood Vessel Segmentation ◽

Multi Scale ◽

Network Training ◽

Average Accuracy

Retinal blood vessel segmentation plays an important role for analysis of retinal diseases, such as diabetic retinopathy and glaucoma. However, retinal blood vessel segmentation remains a challenging task due to the low contrast between some vessels and background, the different presenting conditions caused by uneven illumination and the artificial segmentation results are influenced by human experience, which seriously affects the classification accuracy. To address this problem, we propose a multiple multi-scale neural networks knowledge transfer and integration method in order to accurately segment for retinal blood vessel image. With the integration of multi-scale networks and multi-scale input patches, the blood vessel segmentation performance is obviously improved. In addition, applying knowledge transfer to the network training process, the pre-trained network reduces the number of network training iterations. The experimental results on the DRIVE dataset and the CHASE_DB1 dataset show the effectiveness of the method, whose average accuracy on the two datasets are 96.74% and 97.38%, respectively.

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A New Deeply Convolutional Neural Network Architecture for Retinal Blood Vessel Segmentation

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421570019 ◽

2020 ◽

pp. 2157001

Author(s):

Mali Mohammedhasan ◽

Harun Uğuz

Keyword(s):

Neural Network ◽

Blood Vessel ◽

Convolutional Neural Network ◽

Blood Vessels ◽

Network Architecture ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Low Level ◽

Blood Vessel Segmentation ◽

Retinal Blood Vessels

This paper proposes an incoming Deep Convolutional Neural Network (CNN) architecture for segmenting retinal blood vessels automatically from fundus images. Automatic segmentation performs a substantial role in computer-aided diagnosis of retinal diseases; it is of considerable significance as eye diseases as well as some other systemic diseases give rise to perceivable pathologic changes. Retinal blood vessel segmentation is challenging because of the excessive changes in the morphology of the vessels on a noisy background. Previous deep learning-based supervised methods suffer from the insufficient use of low-level features which is advantageous in semantic segmentation tasks. The proposed architecture makes use of both high-level features and low-level features to segment retinal blood vessels. The major contribution of the proposed architecture concentrates on two important factors; the first in its supplying of extremely modularized network architecture of aggregated residual connections which enable us to copy the learned layers from the shallower model and developing additional layers to identity mapping. The second is to improve the utilization of computing resources within the network. This is achieved through a skillfully crafted design that allows for increased depth and width of the network while maintaining the stability of its computational budget. Experimental results show the effectiveness of using aggregated residual connections in segmenting retinal vessels more accurately and clearly. Compared to the best existing methods, the proposed method outperformed other existing methods in different measures, comprised less false positives at fine vessels, and caressed more clear lines with sufficient details like the human annotator.

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An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

BioMed Research International ◽

10.1155/2020/7352129 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Jianqing Gao ◽

Guannan Chen ◽

Wenru Lin

Keyword(s):

Random Walk ◽

Blood Vessel ◽

Blood Vessels ◽

Morphological Characteristics ◽

Maximum Intensity Projection ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Blood Vessel Segmentation ◽

Retinal Blood Vessels ◽

Structured Analysis

The retinal blood vessel analysis has been widely used in the diagnoses of diseases by ophthalmologists. According to the complex morphological characteristics of the blood vessels in normal and abnormal images, an automatic method by using the random walk algorithms based on the centerlines is proposed to segment retinal blood vessels. Hessian-based multiscale vascular enhancement filtering is used to display the vessel structures in maximum intensity projection. Random walk algorithm provides a unique and quality solution, which is robust to weak object boundaries. Seed groups in the random walk segmentation are labeled according to the centerlines, which are extracted by using the divergence of the normalized gradient vector field and the morphological method. Experiments of the proposed method are implemented on the publicly available STARE (the Structured Analysis of the Retina) database. The results are compared to other existing retinal blood vessel segmentation methods with respect to the accuracy, sensitivity, and specificity, and the proposed method is proved to be more sensitive in detecting the retinal blood vessels in both normal and pathological areas.

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Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network

BioMed Research International ◽

10.1155/2021/5561125 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Yuliang Ma ◽

Zhenbin Zhu ◽

Zhekang Dong ◽

Tao Shen ◽

Mingxu Sun ◽

...

Keyword(s):

Blood Vessel ◽

Blood Vessels ◽

Current Problem ◽

Matched Filter ◽

Vessel Segmentation ◽

Retinal Blood Vessel ◽

Blood Vessel Segmentation ◽

Retinal Blood Vessels ◽

Vessel Enhancement ◽

Better Than

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.

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Retinal blood vessel segmentation based on heuristic image analysis

Computer Science and Information Systems ◽

10.2298/csis180220014b ◽

2019 ◽

Vol 16 (1) ◽

pp. 227-245 ◽

Cited By ~ 4

Author(s):

Maja Braovic ◽

Darko Stipanicev ◽

Ljiljana Seric

Keyword(s):

Blood Vessel ◽

Blood Vessels ◽

Vessel Segmentation ◽

Optic Disk ◽

Fundus Images ◽

Retinal Blood Vessel ◽

Blood Vessel Segmentation ◽

Retinal Blood Vessels ◽

Retinal Fundus Images ◽

Retinal Fundus

Automatic analysis of retinal fundus images is becoming increasingly present today, and diseases such as diabetic retinopathy and age-related macular degeneration are getting a higher chance of being discovered in the early stages of their development. In order to focus on discovering those diseases, researchers commonly preprocess retinal fundus images in order to detect the retinal landmarks - blood vessels, fovea and the optic disk. A large number of methods for the automatic detection of retinal blood vessels from retinal fundus images already exists, but many of them are using unnecessarily complicated approaches. In this paper we demonstrate that a reliable retinal blood vessel segmentation can be achieved with a cascade of very simple image processing methods. The proposed method puts higher emphasis on high specificity (i.e. high probability that the segmented pixels actually belong to retinal blood vessels and are not false positive detections) rather than on high sensitivity. The proposed method is based on heuristically determined parametric edge detection and shape analysis, and is evaluated on the publicly available DRIVE and STARE datasets on which it achieved the average accuracy of 96.33% and 96.10%, respectively.

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