Fundus Image Segmentation Based on Improved Generative Adversarial Network for Retinal Vessel Analysis

He, Jin; Jiang, Dan

doi:10.1109/icaibd49809.2020.9137459

Cited by 11 publications

(9 citation statements)

References 28 publications

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“…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%

“…Their method was based on cGANs with taxonomic diversity and distinction indexes. Although most of the studies are on segmentation tasks and professionally segmented BVs and ODs [ 42 , 78 , 80 , 91 ], they still lack segmenting fine and small vessels and suffer from false positives. By contrast, images synthesizing attracted increased interest of researchers, as it assists in overcoming the shortage of medical images.…”

Section: Resultsmentioning

confidence: 99%

“…Analysis of retinal images, however, must involve trained physicians to analyze and assess digital color fundus images. Such a process requires a great deal of time and human work; therefore, GANs support doctors in mitigating this extensive bottleneck [ 50 , 51 , 91 ]. Furthermore, deep GANs techniques are unlike CNNs, where the same GAN approach could be applied to a wide variety of cases and still produce reasonable results [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

“…U-GAN instead of U-Net should be used to improve the model’s performance [ 49 ]. Additionally, exploiting previous knowledge of vessel structure [ 78 , 80 , 92 ] is critical for accurate segmentation [ 91 ]. Objective function supported with various loss functions may enhance model performance [ 84 ]; for example, WGAN-GP can avoid gradient disappearing and enhance training [ 92 ], Dice coefficient loss function for segmenting hard images [ 57 ], and least-squares loss function with dilated convolution can enhance small vessel segmentation [ 42 ].…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 3 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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Retinal vessel segmentation to diagnose diabetic retinopathy using fundus images: A survey

Radha

Karuna

2023

Int J Imaging Syst Tech

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Diabetes can cause damage to the retina's blood vessels in the eye leading to diabetic retinopathy (DR). The images captured using a fundus camera are used to segment and study the blood vessel damage. Once the retina is damaged, it cannot be repaired. Therefore, early detection of blood vessel damage is the only way to control the progression of the disease. It allows physicians to provide timely and appropriate treatment to patients. The ophthalmologist can manually recognize and mark these vessels based on some clinical and geometrical features; however, it is time‐consuming. Extraction and segmentation play a significant role in showing the difference between healthy and newly developed abnormal vessels. Due to the increased diabetes population, automated systems have been designed to detect retinal blood vessels and assist ophthalmologists. Identifying, extracting, and examining blood vessels are intricate processes, specifically identifying new vessels in the retina. In medical image analysis, artificial intelligence and deep learning techniques have become widely used practices for automatic retinal blood vessel segmentation. We reviewed articles from 1989 to 2023, including handcrafted segmentation to recent deep‐learning techniques with available public datasets. We have concluded this article with an overview of observed parameters, calculations, and future directions for the analysis of retinal images. We believe this review article will help researchers identify research gaps in the field of DR.

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LS-Net: An Improved Deep Generative Adversarial Network for Retinal Lesion Segmentation in Fundus Image

Dayana¹,

Emmanuel²

2022

Lecture Notes in Electrical Engineering

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Fundus Image Segmentation Based on Improved Generative Adversarial Network for Retinal Vessel Analysis

Cited by 11 publications

References 28 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 to diagnose diabetic retinopathy using fundus images: A survey

LS-Net: An Improved Deep Generative Adversarial Network for Retinal Lesion Segmentation in Fundus Image

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