Retinal Vessel Segmentation Based on Adaptive Random Sampling

Jouandeau, Nicolas; Yan, Zhi; Greussay, Patrick; Zou, Beiji; Xiang, Yang

doi:10.12720/jomb.3.3.199-202

Cited by 4 publications

(2 citation statements)

References 8 publications

(7 reference statements)

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“…Oliveira et al [21] used three filters, including Gabor Wavelet filter, matched filter, Frangi's filter, to enhance the blood vessels. Jouandeau et al [22] proposed an segmentation method based on adaptive. Waters et al [23] proposed a method for modeling vessel as trenches.…”

Section: A Unsupervised Methodsmentioning

confidence: 99%

Attention Guided U-Net With Atrous Convolution for Accurate Retinal Vessels Segmentation

Yan

et al. 2020

IEEE Access

106

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The accuracy of retinal vessels segmentation is of great significance for the diagnosis of cardiovascular diseases such as diabetes and hypertension. Especially, the segmentation accuracy of the end of vessels will be affected by the area outside the retinal in fundus image. In this paper, we propose an attention guided U-Net with atrous convolution(AA-UNet), which guides the model to separate vessel and non-vessel pixels and reuses deep features. Firstly, AA-UNet regresses a boundary box to the retinal region to generate an attention mask, which was used as a weighting function to multiply the differential feature map in the model to make the model pay more attention to the vessels region. Secondly, atrous convolution replaces ordinary convolution in feature layer, which can increase the receptive field and reduce the amount of computation. Then, we add two shortcuts to the atrous convolution in order to reuse the features, so that the details of vessel are more prominent. We test our model with the accuracy are 0.9558/0.9640/0.9608 and AUC are 0.9847/0.9824/0.9865 on DRIVE, STARE and CHASE_DB1 datasets, respectively. The results show that our method has improvement in the accuracy of retinal vessels segmentation, and exceeded other representative retinal vessels segmentation methods.

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Section: A Unsupervised Methodsmentioning

confidence: 99%

Attention Guided U-Net With Atrous Convolution for Accurate Retinal Vessels Segmentation

Yan

et al. 2020

IEEE Access

106

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show abstract

“…They took the average of a few performance metrics to enhance the contrast between vessels and background. Jouandeau et al presented an algorithm which was based on an adaptive random sampling algorithm [14]. Garg et al proposed a segmentation approach which modeled the vessels as trenches [15].…”

Section: A Unsupervised Methodsmentioning

confidence: 99%

DUNet: A deformable network for retinal vessel segmentation

Jin,

Meng,

Pham

et al. 2018

Preprint

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Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels' local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level feature maps with highlevel ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels' scales and shapes. Three public datasets DRIVE, STARE and CHASE DB1 are used to train and test our model. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels are extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9697/0.9722/0.9724 and AUC of 0.9856/0.9868/0.9863 on DRIVE, STARE and CHASE DB1 respectively. Moreover, to show the generalization ability of the DUNet, we used another two retinal vessel data sets, one is named WIDE and the other is a synthetic data set with diverse styles, named SYNTHE, to qualitatively and quantitatively analyzed and compared with other methods. Results indicates that DUNet outperforms other state-of-the-arts.

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Automated detection of diseases by nicking quantification in retinal images

Parasuraman

Ramya

2014

2014 International Conference on Electronics, Communication and Computational Engineering (ICECCE)

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Retinal Vessel Segmentation Based on Adaptive Random Sampling

Cited by 4 publications

References 8 publications

Attention Guided U-Net With Atrous Convolution for Accurate Retinal Vessels Segmentation

Attention Guided U-Net With Atrous Convolution for Accurate Retinal Vessels Segmentation

DUNet: A deformable network for retinal vessel segmentation

Automated detection of diseases by nicking quantification in retinal images

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