Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks

Yang, Jingwen; Dong, Xiao; Hu, Yu; Peng, Qingsheng; Tao, Guihua; Ou, Yangming; Cai, Hongming; Yang, Xiaohong

doi:10.1007/s12539-020-00385-5

Cited by 25 publications

(26 citation statements)

References 30 publications

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“…The Patho-GAN can thus generate images of glaucoma fundus with clearer pathologies. In Yang et al [ 76 ], the VGG19 network was incorporated with the 3 players to find the topology structure loss, which was combined with the other 3 losses (adversarial loss, weighted cross-entropy loss, and total variation loss) to be used by the generator. However, in [ 77 ], the authors used 2 encoders, namely, E s and E t , where (s) is the source domain and (t) is the target domain; these encoders were trained to impede the classification performance of the discriminators (D+, D–).…”

Section: Resultsmentioning

confidence: 99%

“…The subsequent layer in our taxonomy was to classify methods according to the generator’s backbone (eg, U-Net based or CNN based) [ 69 ]. 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%

“…As illustrated in Figure 5 , references with P, H, or G letters represent refer to PixelGAN, PatchGAN, or ImageGAN, respectively. ImageGAN papers were [ 42 , 51 , 58 , 80 , 86 , 88 , 90 , 93 , 94 ], while PixelGAN papers were [ 49 , 73 , 74 , 76 - 78 , 82 , 91 , 92 ]. In addition, PatchGAN papers were [ 43 , 46 , 50 , 57 , 75 , 79 , 81 , 83 - 85 , 87 , 89 ].…”

Section: Resultsmentioning

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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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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

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

View full text Add to dashboard Cite

show abstract

“…There are two main schemes for radiomics including deep learning and feature engineering combining classic machine learning methods [8,9]. Deep learning has achieved good results in some image recognition problems and image segmentation problems [34][35][36][37]. But deep learning has substantial difficulties and challenges in AI applications involving small sample sizes, small regions, or expecting interpretability [34,35].…”

Section: Discussionmentioning

confidence: 99%

A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions

Qiu

Peng

Jin

et al. 2021

Interdiscip Sci Comput Life Sci

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Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review

Prasad

Yegneswaran

2023

J Med Syst

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Detection of curvilinear structures from microscopic images, which help the clinicians to make an unambiguous diagnosis is assuming paramount importance in recent clinical practice. Appearance and size of dermatophytic hyphae, keratitic fungi, corneal and retinal vessels vary widely making their automated detection cumbersome. Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background. Automatic feature learning ability using large input data with better generalization and recognition capability, but devoid of human interference and excessive pre-processing, is highly beneficial in the above context. Varied attempts have been made by researchers to overcome challenges such as thin vessels, bifurcations and obstructive lesions in retinal vessel detection as revealed through several publications reviewed here. Revelations of diabetic neuropathic complications such as tortuosity, changes in the density and angles of the corneal fibers have been successfully sorted in many publications reviewed here. Since artifacts complicate the images and affect the quality of analysis, methods addressing these challenges have been described. Traditional and deep learning methods, that have been adapted and published between 2015 and 2021 covering retinal vessels, corneal nerves and filamentous fungi have been summarized in this review. We find several novel and meritorious ideas and techniques being put to use in the case of retinal vessel segmentation and classification, which by way of cross-domain adaptation can be utilized in the case of corneal and filamentous fungi also, making suitable adaptations to the challenges to be addressed.

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Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks

Cited by 25 publications

References 30 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

A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions

Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review

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