ArticleJournal of medical systems2017
A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis.
Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Clinical validation ofIndian journal of ophthalmology · 2019Trial
- Multiscale Attention Unet: An Innovative Approach for Segmentation of Optic Disc and Optic Cup in Early Detection of Retinopathy.Ophthalmology science · 2026Article
- Research on segmentation model of optic disc and optic cup in fundus.BMC ophthalmology · 2024Article
- Joint optic disk and cup segmentation for glaucoma screening using a region-based deep learning network.Eye (London, England) · 2023Article
- Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion.Frontiers in physiology · 2023Article
- Superpixel-Based Optic Nerve Head Segmentation Method of Fundus Images for Glaucoma Assessment.Diagnostics (Basel, Switzerland) · 2022Article
- An Efficient Hierarchical Optic Disc and Cup Segmentation Network Combined with Multi-task Learning and Adversarial Learning.Journal of digital imaging · 2022Article
- Utilizing human intelligence in artificial intelligence for detecting glaucomatous fundus images using human-in-the-loop machine learning.Indian journal of ophthalmology · 2022Article
- The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases.Journal of optometry · 2022Review
- Development of glaucoma predictive model and risk factors assessment based on supervised models.BioData mining · 2021Article
- Modeling and mitigating human annotations to design processing systems with human-in-the-loop machine learning for glaucomatous defects: The future in artificial intelligence.Indian journal of ophthalmology · 2021Article
- Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.Journal of medical systems · 2020Article
- Combination of Enhanced Depth Imaging Optical Coherence Tomography and Fundus Images for Glaucoma Screening.Journal of medical systems · 2019Article
- Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior.Computational and mathematical methods in medicine · 2019Article
- Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease.BioMed research international · 2019Article
- Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening.PloS one · 2019Article
- An efficient optic cup segmentation method decreasing the influences of blood vessels.Biomedical engineering online · 2018Article
- Clinical Applications of Artificial Intelligence in Glaucoma.Journal of ophthalmic & vision researchReview
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.