Retinal OCT Layer Segmentation via Joint Motion Correction and Graph-Assisted 3D Neural Network
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA
2. Jacobs Retina Center, Shiley Eye Institute, University of California, San Diego, CA, USA
Funder
National Eye Institute
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10254538.pdf?arnumber=10254538
Reference38 articles.
1. Clinically applicable deep learning for diagnosis and referral in retinal disease
2. Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns
3. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search
4. Ultrahigh speed Spectral / Fourier domain OCT ophthalmic imaging at 70,000 to 312,500 axial scans per second
5. Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs
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