Retinal Vessel Segmentation Under Extreme Low Annotation: A Gan Based Semi-Supervised Approach

Lahiri, Avisek; Jain, Vineet; Mondal, Arnab Kumar; Biswas, Prabir Kumar

doi:10.1109/icip40778.2020.9190882

Cited by 18 publications

(21 citation statements)

References 17 publications

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“…Papers [ 42 , 46 , 49 , 51 , 57 , 73 , 75 , 76 , 80 - 87 ] represented about 50% of the studies (n=16) and were U-Net-based architectures. However, the other 50% of the papers [ 43 , 46 , 50 , 51 , 58 , 74 , 77 - 79 , 88 - 94 ] were CNN-based generators (n=16).…”

Section: Resultsmentioning

confidence: 99%

“…These references contributed to 20/30 (67%) of the total papers. Seventeen of them were BV-based methods [ 42 , 43 , 49 - 51 , 58 , 75 , 76 , 78 , 81 - 85 , 88 , 91 , 92 , 94 ]. Only 2 studies [ 57 , 81 ] were OD-based detection approaches, and 1 [ 82 ] utilized RNFL-based detection ( Figure 6 ).…”

Section: Resultsmentioning

confidence: 99%

“…However, this can easily lead to underfitting, and as a result, high error rates on both training and testing data are recorded. Lahiri et al [ 43 ] demonstrated the effectiveness of using generative adversarial networks (GANs) [ 44 ] to perform some discriminative task with only 0.8%-1.6% of the amount of annotation data used by other methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Saeed¹,

Abdullah²,

Che-Hamzah³

et al. 2021

J Med Internet Res

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Background Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease using recently developed generative adversarial networks (GANs). Objective This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology. Methods To organize this review comprehensively, articles and reviews were collected using the following keywords: (“Glaucoma,” “optic disc,” “blood vessels”) and (“receptive field,” “loss function,” “GAN,” “Generative Adversarial Network,” “Deep learning,” “CNN,” “convolutional neural network” OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020. Results We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease. Conclusions Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Saeed¹,

Abdullah²,

Che-Hamzah³

et al. 2021

J Med Internet Res

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show abstract

“…Lahiri, et al [150] also trained a GAN based on semisupervised learning to learn from both labeled and unlabeled data. They only used 3K annotated image patches to make patch-wise predictions and obtained 0.95/0.96 accuracy and 0.96/0.94 AUC on DRIVE and STARE databases, respectively.…”

Section: E Generative Adversarial Network (Gan) For Retinal Vessel Segmentationmentioning

confidence: 99%

Retinal Vessel Segmentation Using Deep Learning: A Review

et al. 2021

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This paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning. The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension. The accurate diagnosis and timing treatment of these diseases can prevent global blindness of patients. Recently, deep learning algorithms have been rapidly applied to retinal vessel segmentation due to their higher efficiency and accuracy, when compared with manual segmentation and other computer-aided diagnosis techniques. In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. We surveyed these proposed methods especially the network architectures and figured out the trend of models. We summarized obstacles and key aspects for applying deep learning to retinal vessel segmentation and indicated future research directions. This article will help researchers to construct more advanced and robust models.INDEX TERMS Retinal vessel segmentation, fundus images, deep learning, convolutional neural network.

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“…In reality, the task of labeling medical images is high-cost and laborious, especially for retinal vascular images [7]- [10]. Chen et al [11] proposed a semi-supervised method combined with a generative adversarial network(GAN) to correct pseudo labels by discriminators, which can achieve decent segmentation results with a small amount of labeled data. Xu et al [12] proposed a partially supervised framework with an active learning strategy to label the patches with the most informative, reducing the dependence on labeled data.…”

Section: Introductionmentioning

confidence: 99%

SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

Ma¹

2021

Preprint

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We propose a SSL-Unet model for retinal vascular segmentation as well as two self-supervised training strategies. The strategy can help the self-supervised module to learn pseudo labels for improving the segmentation performance. Moreover, the fusion of both self-supervised and supervised paradigms is applied to retinal segmentation for the first time. Meanwhile, it can also be extended to any segmentation network.

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Retinal Vessel Segmentation Under Extreme Low Annotation: A Gan Based Semi-Supervised Approach

Cited by 18 publications

References 17 publications

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Retinal Vessel Segmentation Using Deep Learning: A Review

SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

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