Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images | Zendy

Zexuan Ji | Zendy; Qiang Chen | Zendy; Sijie Niu | Zendy; Theodore Leng | Zendy; Daniel L. Rubin | Zendy

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Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images

Author(s) -

Zexuan Ji,

Qiang Chen,

Sijie Niu,

Theodore Leng,

Daniel L. Rubin

Publication year - 2018

Publication title -

translational vision science and technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.508

H-Index - 21

ISSN - 2164-2591

DOI - 10.1167/tvst.7.1.1

Subject(s) - segmentation , geographic atrophy , retinal , artificial intelligence , optical coherence tomography , atrophy , voting , ophthalmology , computer science , pattern recognition (psychology) , anatomy , cartography , medicine , optometry , computer vision , pathology , geography , politics , political science , law

Based on the deep neural networks, this study presents an accurate GA segmentation method for SD-OCT images without using any retinal layer segmentation results, and may contribute to improved understanding of advanced nonexudative AMD.

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