Retinal vessel segmentation based on Generative Adversarial network and Dilated convolution

Ma, Jun; Meng, Wei; Ma, Ziping; Shi, Li; Zhu, Kai

doi:10.1109/iccse.2019.8845491

Cited by 12 publications

(21 citation statements)

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“…Following [ 66 - 68 ], we added a third layer to our taxonomy to classify papers as either structure-based or optimization-based methods. The majority of studies (27/30, 90%) at this level were structure- and conditional-based methods, while only 3/30 (10%) of the studies, namely, those in [ 42 , 78 , 79 ], were optimization-based methods with 2-player structures; none of these methods have been recorded as multiplayer-based structures.…”

Section: Resultsmentioning

confidence: 99%

“…Some researchers tend to use objective function–based methods by updating specific loss functions or using a combination of losses to overcome the model collapse of GANs. This occurs when the generator continuously generates images with the same distribution or generates images with the same texture themes or color as the original image but with marginal differences in human understanding [ 65 ]; for example, Ma et al [ 42 ] used a least-squares loss function instead of sigmoid cross-entropy. Therefore, their experiment greatly improved the segmentation accuracy of the utilized model on both the digital retinal image for vessels extraction (DRIVE) and structured analysis of the retina (STARE) data sets by forcing the generator to generate images with distributions close to those of the real images.…”

Section: Resultsmentioning

confidence: 99%

“…To win the game, both G and D need to continuously improve their generation and discrimination capabilities, respectively. The training process lasts until both G and D reach a convergence point (Nash equilibrium), where G generates an output distribution very close to the real data distribution [ 42 , 57 , 58 ].…”

Section: Methodsmentioning

confidence: 99%

“…Such methods, however, create very fuzzy vessels with false positives along with tiny and weak roots. This error primarily occurs because the CNNs used in current methods depend solely on pixel-level objective feature to equate the standard image to the image created by the model and are incapable of adapting actively to the fundus image of the natural vascular structure [ 42 ]. Empirical studies have proven that deep CNNs can learn invariant representations and attain human-level success if sufficient training data are provided.…”

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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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: Methodsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Most generative models adopted encoder-decoder structure with some improvement modules, such as dense block [137], Wu, et al [138], and dilated convolution [137,139], deep supervision [140], attention mechanism [138,141], skip connections [142], Inception module [143] and others [144]. CNNs were widely used as generators [137-139, 142, 144-146], but U-net can also be adopted [141,143].…”

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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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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Retinal vessel segmentation based on Generative Adversarial network and Dilated convolution

Cited by 12 publications

References 1 publication

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

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

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