Automated segmentation of optic disc in SD-OCT images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection-Reference-Cited by-同舟云学术

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Automated segmentation of optic disc in SD-OCT images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection

Published:2015-11-20 Issue:24 Volume:23 Page:31216
ISSN:1094-4087
Container-title:Optics Express
language:en
Short-container-title:Opt. Express

Author:

Wu Menglin,Leng Theodore,de Sisternes Luis,Rubin Daniel L.,Chen Qiang

Funder

Fundamental Research Funds for the Central Universities

Qing Lan Project

six talent peaks project in Jiangsu Province

National Natural Science Foundation of China (NSFC)

Publisher

The Optical Society

Subject

Atomic and Molecular Physics, and Optics

Reference28 articles.

1. Detection of Optic Nerve Head Neural Canal Opening within Histomorphometric and Spectral Domain Optical Coherence Tomography Data Sets

2. Comparison of optic nerve head measurements obtained by optical coherence tomography and confocal scanning laser ophthalmoscopy

3. Variance Owing to Observer, Repeat Imaging, and Fundus Camera Type on Cup-to-disc Ratio Estimates by Stereo Planimetry

4. Automated Segmentation of the Cup and Rim from Spectral Domain OCT of the Optic Nerve Head

5. Automated volumetric evaluation of stereoscopic disc photography

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1. Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia;Eye;2024-03-21

2. Cup-Disk Ratio Segmentation Joint with Key Retinal Vascular Information Under Diagnostic and Screening Scenarios;Advances in Computer Graphics;2023-12-24

3. End-to-end algorithm for the automatic detection of the neural canal opening in OCT images based on a multi-task deep learning model;Optics Continuum;2023-09-11

4. PKRT-Net: Prior knowledge-based relation transformer network for optic cup and disc segmentation;Neurocomputing;2023-06

5. Applications of Artificial Intelligence and Deep Learning in Glaucoma;Asia-Pacific Journal of Ophthalmology;2023-01

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