ArticleBiomedical optics express2019
Joint retina segmentation and classification for early glaucoma diagnosis.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review.European journal of ophthalmology · 2023Pooled it
- Automatic segmentation of layers in chorio-retinal complex using Graph-based method for ultra-speed 1.7 MHz wide field swept source FDML optical coherence tomography.Medical & biological engineering & computing · 2024Article
- Self-attention CNN for retinal layer segmentation in OCT.Biomedical optics express · 2024Article
- Cost-efficient and glaucoma-specifical model by exploiting normal OCT images with knowledge transfer learning.Biomedical optics express · 2023Article
- Longitudinal deep network for consistent OCT layer segmentation.Biomedical optics express · 2023Article
- Tissue self-attention network for the segmentation of optical coherence tomography images on the esophagus.Biomedical optics express · 2021Article
- Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images.Biomedical optics express · 2021Article
- Article
- Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies.Contrast media & molecular imaging · 2021Article
- High Precision Mammography Lesion Identification From Imprecise Medical Annotations.Frontiers in big data · 2021Article
- Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning.Translational vision science & technology · 2020Article
- Adversarial convolutional network for esophageal tissue segmentation on OCT images.Biomedical optics express · 2020Article
- Clinical Applications of Artificial Intelligence in Glaucoma.Journal of ophthalmic & vision researchReview
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose a joint segmentation and classification deep model for early glaucoma diagnosis using retina imaging with optical coherence tomography (OCT). Our motivation roots in the observation that ophthalmologists make the clinical decision by analyzing the retinal nerve fiber layer (RNFL) from OCT images. To simulate this process, we propose a novel deep model that joins the retinal layer segmentation and glaucoma classification. Our model consists of three parts. First, the segmentation network simultaneously predicts both six retinal layers and five boundaries between them. Then, we introduce a post processing algorithm to fuse the two results while enforcing the topology correctness. Finally, the classification network takes the RNFL thickness vector as input and outputs the probability of being glaucoma. In the classification network, we propose a carefully designed module to implement the clinical strategy to diagnose glaucoma. We validate our method both in a collected dataset of 1004 circular OCT B-Scans from 234 subjects and in a public dataset of 110 B-Scans from 10 patients with diabetic macular edema. Experimental results demonstrate that our method achieves superior segmentation performance than other state-of-the-art methods both in our collected dataset and in public dataset with severe retina pathology. For glaucoma classification, our model achieves diagnostic accuracy of 81.4% with AUC of 0.864, which clearly outperforms baseline methods.
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Registered trials
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