Retinal Blood Vessel Segmentation using Deep Learning

2019 ◽  
Vol 17 (5) ◽  
pp. 77-82 ◽  
Author(s):  
Beomsang Kim ◽  
Ik Hyun Lee
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Retinal Blood Vessel ◽  
Blood Vessel Segmentation

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 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 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.

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