Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Soomro, Toufique Ahmed; Afifi, Ahmed J.; Zheng, Lihong; Soomro, Shafiullah; Gao, Junbin; Hellwich, Olaf; Paul, Manoranjan

doi:10.1109/access.2019.2920616

Cited by 123 publications

(62 citation statements)

References 61 publications

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“…To evaluate the performance of the proposed models, more performance matrices need to be investigated through this research. The most common performance measures in the field of DL are precision, recall, and F1 score (38), which are presented from equation (1) to equation 3, respectively.…”

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

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Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

Khalifa¹,

Loey²,

Taha³

et al. 2019

Acta Inform Med

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Introduction: Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic patients will increase to 552 million by 2034, as per the International Diabetes Federation (IDF). Aim: With advances in computer science techniques, such as artificial intelligence (AI) and deep learning (DL), opportunities for the detection of DR at the early stages have increased. This increase means that the chances of recovery will increase and the possibility of vision loss in patients will be reduced in the future. Methods: In this paper, deep transfer learning models for medical DR detection were investigated. The DL models were trained and tested over the Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset. According to literature surveys, this research is considered one the first studies to use of the APTOS 2019 dataset, as it was freshly published in the second quarter of 2019. The selected deep transfer models in this research were AlexNet, Res-Net18, SqueezeNet, GoogleNet, VGG16, and VGG19. These models were selected, as they consist of a small number of layers when compared to larger models, such as DenseNet and InceptionResNet. Data augmentation techniques were used to render the models more robust and to overcome the overfitting problem. Results: The testing accuracy and performance metrics, such as the precision, recall, and F1 score, were calculated to prove the robustness of the selected models. The AlexNet model achieved the highest testing accuracy at 97.9%. In addition, the achieved performance metrics strengthened our achieved results. Moreover, AlexNet has a minimum number of layers, which decreases the training time and the computational complexity.

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Section: Performance Evaluation and Discussionmentioning

confidence: 99%

“…Diabetic retinopathy (DR) is a diabetic disease of the eye in which the retinal blood vessels of people's eyes are damaged because of long-standing Diabetes mellitus (1,2). As estimated by the International Diabetes Federation (IDF), the number of diabetic patients will increase to 552 million by 2035 (3).…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

Khalifa¹,

Loey²,

Taha³

et al. 2019

Acta Inform Med

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“…As a whole, each block in the feature fusion decoder was also a repeating structure of up-sampling, followed by multiple 3 × 3 deconvolutions, Batch Normalization (BN), and leaky ReLU activation operations. Simultaneously, the MSFFU-Net contained extended two skip connections: one was that each set of feature maps generated on the encoder path are concatenated to the corresponding feature maps on the decoder path; the other was that transferring of max pooling indices values from the encoder to the decoder to locate contour position information of multi-scale retinal vessel features for higher segmentation accuracy [ 32 ]. The feature maps of the upsampling operation were merged with the corresponding output feature maps of the two extended skip modules [ 33 ], as shown in Figure 6 .…”

Section: Proposed Methodsmentioning

confidence: 99%

A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation

Yang

Liu

Ren

et al. 2020

Entropy

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Computer-aided automatic segmentation of retinal blood vessels plays an important role in the diagnosis of diseases such as diabetes, glaucoma, and macular degeneration. In this paper, we propose a multi-scale feature fusion retinal vessel segmentation model based on U-Net, named MSFFU-Net. The model introduces the inception structure into the multi-scale feature extraction encoder part, and the max-pooling index is applied during the upsampling process in the feature fusion decoder of an improved network. The skip layer connection is used to transfer each set of feature maps generated on the encoder path to the corresponding feature maps on the decoder path. Moreover, a cost-sensitive loss function based on the Dice coefficient and cross-entropy is designed. Four transformations—rotating, mirroring, shifting and cropping—are used as data augmentation strategies, and the CLAHE algorithm is applied to image preprocessing. The proposed framework is tested and trained on DRIVE and STARE, and sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under curve (AUC) are adopted as the evaluation metrics. Detailed comparisons with U-Net model, at last, it verifies the effectiveness and robustness of the proposed model. The Sen of 0.7762 and 0.7721, Spe of 0.9835 and 0.9885, Acc of 0.9694 and 0.9537 and AUC value of 0.9790 and 0.9680 were achieved on DRIVE and STARE databases, respectively. Results are also compared to other state-of-the-art methods, demonstrating that the performance of the proposed method is superior to that of other methods and showing its competitive results.

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“…It is a powerful tool for image segmentation [37]. With retinal vessel segmentation, deep learning model is arranged and calculated clearly as [36,38,39]. where, z i is the pixel ith joins into networks.…”

Section: Features Extraction By Convolutional Neural Network In Saliementioning

confidence: 99%

“…In the state-of-the art life, the powerful develop of deep learning speaks up which it overcomes the limit in feature extraction. As a result, in retinal vessel segmentation is not out of that rule [36][37][38][39]. However, these solutions are complex about the parameter and must apply in global image.…”

Section: Introductionmentioning

confidence: 99%

Improving Retinal Vessels Segmentation via Deep Learning in Salient Region

Tuyet

Binh

2020

SN COMPUT. SCI.

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Retinal vessels segmentation is one of the hard tasks. Because it may cause loss of information in the image. Therefore, the features of retinal vessel must be taken care of properly. Deep learning gives the positive results for features extraction. The retinal vessel segmentation really needs to be based on these features. Information in retina is also vital for the health status of citizen. However, the using deep learning combined with salient region will increase the accuracy of saliency. The number of parameters and size for dividing level in each input images make pixels decrease linking. This paper proposed a new method for retinal vessels segmentation based on deep learning in edge map which is created by salient map. The proposed method has three periods: edges salient map in the retinal vessel image, features extraction by convolutional neural network in salient map and segmentation based on pixel level of Sobel operator in saliency. To evaluate the results of proposed method with the other methods, we use the DRIVE dataset through the Jaccard index value. The experiments show that the JI value of the proposed method is higher than the results in other methods.

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Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Cited by 123 publications

References 61 publications

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection

A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation

Improving Retinal Vessels Segmentation via Deep Learning in Salient Region

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