scholarly journals Spider U-Net: Incorporating Inter-Slice Connectivity Using LSTM for 3D Blood Vessel Segmentation

2021 ◽  
Vol 11 (5) ◽  
pp. 2014
Author(s):  
Kyeorye Lee ◽  
Leonard Sunwoo ◽  
Tackeun Kim ◽  
Kyong Joon Lee
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Short Term Memory ◽  
Feeding Strategy ◽  
Model Performance ◽  
Vessel Segmentation ◽  
Medical Image Segmentation ◽  
Convolutional Network ◽  
Blood Vessel Segmentation ◽  
Public Datasets

Blood vessel segmentation (BVS) of 3D medical imaging such as computed tomography and magnetic resonance angiography (MRA) is an essential task in the clinical field. Automation of 3D BVS using deep supervised learning is being researched, and U-Net-based approaches, which are considered as standard for medical image segmentation, are proposed a lot. However, the inherent characteristics of blood vessels, e.g., they are complex and narrow, as well as the resolution and sensitivity of the imaging modalities increases the difficulty of 3D BVS. We propose a novel U-Net-based model named Spider U-Net for 3D BVS that considers the connectivity of the blood vessels between the axial slices. To achieve this, long short-term memory (LSTM), which can capture the context of the consecutive data, is inserted into the baseline model. We also propose a data feeding strategy that augments data and makes Spider U-Net stable. Spider U-Net outperformed 2D U-Net, 3D U-Net, and the fully convolutional network-recurrent neural network (FCN-RNN) in dice coefficient score (DSC) by 0.048, 0.077, and 0.041, respectively, for our in-house brain MRA dataset and also achieved the highest DSC for two public datasets. The results imply that considering inter-slice connectivity with LSTM improves model performance in the 3D BVS task.

scholarly journals Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuliang Ma ◽  
Xue Li ◽  
Xiaopeng Duan ◽  
Yun Peng ◽  
Yingchun Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Area Under The Curve ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Small Vessels

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.

Morphological Contour Based Blood Vessel Segmentation in Retinal Images Using Otsu Thresholding

2018 ◽  
Vol 9 (4) ◽  
pp. 48-63 ◽  
Author(s):  
S. Saranya Rubini ◽  
A. Kunthavai ◽  
M.B. Sachin ◽  
S. Deepak Venkatesh
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Early Stage ◽  
Histogram Equalization ◽  
Vessel Segmentation ◽  
Contour Detection ◽  
Experimental Result ◽  
Blood Vessel Segmentation ◽  
Otsu Thresholding ◽  
Sensitivity Specificity

Retinal image analysis plays an important part in identifying various eye related diseases such as diabetic retinopathy (DR), glaucoma and many others. Accurate segmentation of blood vessels plays an important part in identifying the retinal diseases at an early stage. In this article, an unsupervised approach based on contour detection has been proposed for effective segmentation of retinal blood vessels. The proposed morphological contour-based blood vessel segmentation (MCBVS) method performs preprocessing using contrast limited adaptive histogram equalization followed by alternate sequential filtering to generate a noise-free image. The resultant image undergoes Otsu thresholding for candidate extraction followed by contour detection to properly segment the blood vessels. The MCBVS method has been tested on the DRIVE dataset and the experimental result shows that the proposed method achieved a sensitivity, specificity and accuracy of 58.79%, 90.77% and 86.7%, respectively. The MCBVS method performs better than the existing methods Sobel, Prewitt and Modified U-Net in terms of accuracy.

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

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>

scholarly journals Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Feng Tian ◽  
Ying Li ◽  
Jing Wang ◽  
Wei Chen
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Vessel Segmentation ◽  
Retinal Images ◽  
Threshold Method ◽  
Complexity Of Algorithms ◽  
Morphological Method ◽  
Blood Vessel Segmentation ◽  
Segmentation Accuracy ◽  
Improved Algorithm

An improved blood vessel segmentation algorithm on the basis of traditional Frangi filtering and the mathematical morphological method was proposed to solve the low accuracy of automatic blood vessel segmentation of fundus retinal images and high complexity of algorithms. First, a global enhanced image was generated by using the contrast-limited adaptive histogram equalization algorithm of the retinal image. An improved Frangi Hessian model was constructed by introducing the scale equivalence factor and eigenvector direction angle of the Hessian matrix into the traditional Frangi filtering algorithm to enhance blood vessels of the global enhanced image. Next, noise interferences surrounding small blood vessels were eliminated through the improved mathematical morphological method. Then, blood vessels were segmented using the Otsu threshold method. The improved algorithm was tested by the public DRIVE and STARE data sets. According to the test results, the average segmentation accuracy, sensitivity, and specificity of retinal images in DRIVE and STARE are 95.54%, 69.42%, and 98.02% and 94.92%, 70.19%, and 97.71%, respectively. The improved algorithm achieved high average segmentation accuracy and low complexity while promising segmentation sensitivity. This improved algorithm can segment retinal vessels more accurately than other algorithms.

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

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.

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

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.

scholarly journals Optimizing the trainable B-COSFIRE filter for retinal blood vessel segmentation

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5855 ◽  
Author(s):  
Sufian A. Badawi ◽  
Muhammad Moazam Fraz
Keyword(s):  
Blood Vessel ◽  
Optimization Procedure ◽  
Vessel Segmentation ◽  
Automated System ◽  
Suggested Approach ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels ◽  
Analysis Technique ◽  
Public Datasets ◽  
Performance Results

Segmentation of the retinal blood vessels using filtering techniques is a widely used step in the development of an automated system for diagnostic retinal image analysis. This paper optimized the blood vessel segmentation, by extending the trainable B-COSFIRE filter via identification of more optimal parameters. The filter parameters are introduced using an optimization procedure to three public datasets (STARE, DRIVE, and CHASE-DB1). The suggested approach considers analyzing thresholding parameters selection followed by application of background artifacts removal techniques. The approach results are better than the other state of the art methods used for vessel segmentation. ANOVA analysis technique is also used to identify the most significant parameters that are impacting the performance results (p-value ¡ 0.05). The proposed enhancement has improved the vessel segmentation accuracy in DRIVE, STARE and CHASE-DB1 to 95.47, 95.30 and 95.30, respectively.

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

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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