Towards an extended EfficientNet-based U-Net framework for joint optic disc and cup segmentation in the fundus image

Wang, Jinke; Li, Xiang; Ye, Cheng

doi:10.1016/j.bspc.2023.104906

Cited by 18 publications

(4 citation statements)

References 33 publications

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“…The algorithm exhibits superior performance compared with U-Net and several variants trained on a single annotation and traditional training strategies. Wang, Li & Cheng (2023) proposed an extended EfficientNet-based U-Net called EE-U-Net to improve the segmentation accuracy. Quantitative and qualitative results validated the superior performance of EE-U-Net in OC and optic disc segmentation, demonstrating that it was a promising tool for early screening of large-scale glaucoma.…”

Section: Related Workmentioning

confidence: 99%

Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism

Chen,

Bai,

Zhang

2024

PeerJ Computer Science

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Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model’s ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity.

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Section: Related Workmentioning

confidence: 99%

Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism

Chen,

Bai,

Zhang

2024

PeerJ Computer Science

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“…U-Net is designed to overcome feature loss occurring during feature transfer in the decoder, and it can also handle the problem of multiscale features and has enhanced feature reservation capabilities. Since its introduction, many studies have been conducted with U-Net structures [ 23 , 24 , 25 , 26 , 27 , 28 ]. Given these advantages, the proposed approach is based on a U-Net backbone network.…”

Section: Related Workmentioning

confidence: 99%

Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module

et al. 2023

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In recent years, deep learning technology for clinical diagnosis has progressed considerably, and the value of medical imaging continues to increase. In the past, clinicians evaluated medical images according to their individual expertise. In contrast, the application of artificial intelligence technology for automatic analysis and diagnostic assistance to support clinicians in evaluating medical information more efficiently has become an important trend. In this study, we propose a machine learning architecture designed to segment images of retinal blood vessels based on an improved U-Net neural network model. The proposed model incorporates a residual module to extract features more effectively, and includes a full-scale skip connection to combine low level details with high-level features at different scales. The results of an experimental evaluation show that the model was able to segment images of retinal vessels accurately. The proposed method also outperformed several existing models on the benchmark datasets DRIVE and ROSE, including U-Net, ResUNet, U-Net3+, ResUNet++, and CaraNet.

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“…To achieve this, they built a probabilistic graphical model and explored a Bayesian approach with the state-of-the-art U-Net framework [ 33 ]. Wang et al extended the EfficientNet-based U-Net, named EE-U-Net, for OD and OC segmentation [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

Identifying the Edges of the Optic Cup and the Optic Disc in Glaucoma Patients by Segmentation

Tadisetty,

Chodavarapu,

Jin

et al. 2023

Sensors

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With recent advancements in artificial intelligence, fundus diseases can be classified automatically for early diagnosis, and this is an interest of many researchers. The study aims to detect the edges of the optic cup and the optic disc of fundus images taken from glaucoma patients, which has further applications in the analysis of the cup-to-disc ratio (CDR). We apply a modified U-Net model architecture on various fundus datasets and use segmentation metrics to evaluate the model. We apply edge detection and dilation to post-process the segmentation and better visualize the optic cup and optic disc. Our model results are based on ORIGA, RIM-ONE v3, REFUGE, and Drishti-GS datasets. Our results show that our methodology obtains promising segmentation efficiency for CDR analysis.

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Towards an extended EfficientNet-based U-Net framework for joint optic disc and cup segmentation in the fundus image

Cited by 18 publications

References 33 publications

Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism

Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism

Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module

Identifying the Edges of the Optic Cup and the Optic Disc in Glaucoma Patients by Segmentation

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