A Retinal Vessel Segmentation Algorithm with Convolutional Neural Network

2019 ◽  
pp. 497-502
Author(s):  
Leiming Liu ◽  
Jiahao Li ◽  
Weicun Zhang ◽  
Dongmei Fu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Segmentation Algorithm ◽  
Retinal Vessel Segmentation

Construction of Retinal Vessel Segmentation Models Based on Convolutional Neural Network

2019 ◽  
Vol 52 (2) ◽  
pp. 1005-1022 ◽  
Author(s):  
Qiangguo Jin ◽  
Qi Chen ◽  
Zhaopeng Meng ◽  
Bing Wang ◽  
Ran Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Retinal Vessel Segmentation ◽  
Segmentation Models

scholarly journals Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Xu ◽  
Zhiqiang Chen ◽  
Weiyi Cao ◽  
Feng Zhang ◽  
Bo Tao
Keyword(s):  
Neural Network ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Segmentation Algorithm ◽  
Convolution Neural Network ◽  
Superior Performance ◽  
Retinal Vessels ◽  
Fundus Images ◽  
Data Set ◽  
Retinal Vessel Segmentation

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.

Multi-Scale Retinal Vessel Segmentation Based on Fully Convolutional Neural Network

2019 ◽  
Vol 39 (2) ◽  
pp. 0211002 ◽  
Author(s):  
郑婷月 Zheng Tingyue ◽  
唐晨 Tang Chen ◽  
雷振坤 Lei Zhenkun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Multi Scale ◽  
Retinal Vessel Segmentation

scholarly journals Retinal Vessel Segmentation based on Convolutional Neural Network and Connection Domain Detection

2021 ◽  
Vol 187 ◽  
pp. 246-251
Author(s):  
Quansheng Dou ◽  
Jiayuan Zhang ◽  
Ping Jiang ◽  
Huanling Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Retinal Vessel Segmentation
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