Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation

Li, Yang; Zhang, Yue; Cui, Weigang; Lei, Baiying; Kuang, Xihe; Zhang, Teng

doi:10.1109/tmi.2022.3151666

Cited by 90 publications

(26 citation statements)

References 73 publications

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“…As a result, it is undeniably established that MGACO is a very good swarm intelligence optimization algorithm and that MGACO-MIS is an even better segmentation technique when MGACO is used to segment problematic pictures from COVID-19. In future work, the proposed method can also be applied to more cases, such as the optimization of machine learning models iris segmentation and recognition (Chen et al, 2023), fine-grained alignment (Wang et al, 2023), remote pulse extraction (Zhao et al, 2022), Alzheimer's disease identification (Yan et al, 2022), MRI reconstruction (Lv et al, 2021), renewable energy generation (Sun et al, 2022), power distribution network (Cao et al, 2022), retinal vessel segmentation (Li et al, 2022), privacy protection of personalized information retrieval (Wu Z. et al, 2020;Wu et al, 2021cWu et al, ,d, 2023, and privacy protection of location-based services (Wu et al, 2021b(Wu et al, , 2022.…”

Section: Discussionmentioning

confidence: 99%

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

Zhao

Liu

Heidari

et al. 2023

Front. Neuroinform.

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The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur’s entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.

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Section: Discussionmentioning

confidence: 99%

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

Zhao

Liu

Heidari

et al. 2023

Front. Neuroinform.

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“…As a result, the bSCBA-KELM model is anticipated to be a reliable and effective tool for classifying and predicting toxicology. The SCBA-KELM model will be used in further research to address problems with disease diagnosis, 56,57 image segmentation, 58,59 image reconstruction, 60,61 optimization of machine learning models, service ecosystem, 62 power distribution network, 19 computational experiments, 63 and so on.…”

Section: Discussionmentioning

confidence: 99%

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Su,

Zhao,

Heidari

et al. 2023

Basic Clin Pharma Tox

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Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover, and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2, and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.

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“…Downsampling causes edge information loss in vessel segmentation using deep neural networks, Li et al 136 introduced a dual encoder to preserve the edge information. A dedicated edge enhancement block helps to fuse the edge and spatial information.…”

Section: Automatic Retinal Blood Vessel Segmentation Techniques Using...mentioning

confidence: 99%

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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Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation

Cited by 90 publications

References 73 publications

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

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

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