Retinal vessel segmentation based on Fully Convolutional Neural Networks

Oliveira, Américo; Pereira, Sérgio; Silva, Carlos A.

doi:10.1016/j.eswa.2018.06.034

Cited by 264 publications

(153 citation statements)

References 31 publications

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“…Commonly used data augmentation methods are rotation, flipping, cropping, adding noise, and translation. However, as explained in [27], methods such as continuous rotation make the network more difficult to detect blood vessel segments.…”

Section: A Novel Retinal Image Data Augmentation Methodsmentioning

confidence: 99%

“…However, the number of existing retinal image datasets is insufficient to support the training model. Inspired by [2,19,21,27], in our approach, patch-based learning strategies get patches differently that are fed to the network, depending on the stage of the framework. To solve the problem of insufficient memory during training, we dynamically extract a small number of patches in the loop training.…”

Section: Dynamic Patch Extractionmentioning

confidence: 99%

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Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

et al. 2019

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Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the symmetrical and asymmetrical patterns between blood vessels are complicated, and the contrast between the vessel and the background is relatively low due to illumination and pathology. Robust vessel segmentation of the retinal image is essential for improving the diagnosis of diseases such as vein occlusions and diabetic retinopathy. Automated retinal vein segmentation remains a challenging task. In this paper, we proposed an automatic retinal vessel segmentation framework using deep fully convolutional neural networks (FCN), which integrate novel methods of data preprocessing, data augmentation, and full convolutional neural networks. It is an end-to-end framework that automatically and efficiently performs retinal vessel segmentation. The framework was evaluated on three publicly available standard datasets, achieving F1 score of 0.8321, 0.8531, and 0.8243, an average accuracy of 0.9706, 0.9777, and 0.9773, and average area under the Receiver Operating Characteristic (ROC) curve of 0.9880, 0.9923 and 0.9917 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. The experimental results show that our proposed framework achieves state-of-the-art vessel segmentation performance in all three benchmark tests.

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Section: A Novel Retinal Image Data Augmentation Methodsmentioning

confidence: 99%

Section: Dynamic Patch Extractionmentioning

confidence: 99%

Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

et al. 2019

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“…Recently, Zhang et al [54] introduced an edge-aware mechanism to convert the task into a multi-class task. Oliveira et al [55] and Wu et al [56] proposed to segment retinal vessels based on conventional FCN and multi-scale architecture, respectively. Compared with [54][56], we have taken the vessel connectivity into consideration using probability regularized walk, however [54]- [56] and other methods have not taken this into account.…”

Section: B Vessel Detectionmentioning

confidence: 99%

Dense Dilated Network With Probability Regularized Walk for Vessel Detection

Mou

Chen

Cheng

et al. 2020

IEEE Trans. Med. Imaging

134

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The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. However, most of the algorithms neglect the connectivity of the vessels, which plays an important role in the diagnosis. In this paper, we propose a novel method for retinal vessel detection. The proposed method includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection. The dense dilated network integrates newly proposed dense dilated feature extraction blocks into an encoder-decoder structure to extract and accumulate features at different scales. A multiscale Dice loss function is adopted to train the network. To improve the connectivity of the segmented vessels, we also introduce a probability regularized walk algorithm to connect the broken vessels. The proposed method has been applied on three public data sets: DRIVE, STARE and CHASE DB1. The results show that the proposed method outperforms the state-ofthe-art methods in accuracy, sensitivity, specificity and also area under receiver operating characteristic curve.

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“…Thus, BVs were segmented and eliminated to minimize the influence on MA detection. The most widely used BV segmentation methods are machine learning [35] and deep learning [36,37], which have high accuracy but are complicated and timeconsuming. Inspired by Jerman et al 's work [33], which was improved from the widely used method for BV enhancement of Frangi et al 's work [34], this study constructed a response function to enhance BV by analyzing the eigenvalues of Hessian matrix, to achieve simple, fast, and accurate BV segmentation.…”

Section: Segmentation Of Blood Vesselsmentioning

confidence: 99%

Microaneurysms detection in color fundus images using machine learning based on directional local contrast

Long

Chen

et al. 2020

BioMed Eng OnLine

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Background: As one of the majorcomplications of diabetes, diabetic retinopathy (DR) is a leadingcause of visual impairment and blindness due to delayed diagnosisand intervention. Microaneurysms appear as the earliest symptom ofDR. Accurate and reliable detection of microaneurysms in colorfundus images has great importance for DR screening.Methods: A microaneurysms' detection methodusing machine learning based on directional local contrast (DLC) isproposed for the early diagnosis of DR. First, blood vessels wereenhanced and segmented using improved enhancement function based onanalyzing eigenvalues of Hessian matrix. Next, with blood vesselsexcluded, microaneurysm candidate regions were obtained using shapecharacteristics and connected components analysis. After imagesegmented to patches, the features of each microaneurysm candidatepatch were extracted, and each candidate patch was classified intomicroaneurysm or non-microaneurysm. The main contributions of ourstudy are (1) making use of directional local contrast inmicroaneurysms' detection for the first time, which does make sensefor better microaneurysms' classification.(2) Applying threedifferent machine learning techniques for classification andcomparing their performance for microaneurysms' detection. Theproposed algorithm was trained and tested on e-ophtha MA database,and further tested on another independent DIARETDB1 database.Results of microaneurysms' detection on the two databases wereevaluated on lesion level and compared with existing algorithms. Results:The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively. Conclusions:The proposed methodusing machine learning based on directional local contrast of imagepatches can effectively detect microaneurysms in color fundus imagesand provide an effective scientific basis for early clinical DRdiagnosis.

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Retinal vessel segmentation based on Fully Convolutional Neural Networks

Cited by 264 publications

References 31 publications

Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

Dense Dilated Network With Probability Regularized Walk for Vessel Detection

Microaneurysms detection in color fundus images using machine learning based on directional local contrast

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