4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans | Zendy

İpek Oğuz | Zendy; Michael D. Abràmoff | Zendy; Li Zhang | Zendy; Kyungmoo Lee | Zendy; Ellen Ziyi Zhang | Zendy; Milan Sonka | Zendy

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4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans

Author(s) -

İpek Oğuz,

Michael D. Abràmoff,

Li Zhang,

Kyungmoo Lee,

Ellen Ziyi Zhang,

Milan Sonka

Publication year - 2016

Publication title -

investigative ophthalmology and visual science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.935

H-Index - 218

eISSN - 1552-5783

pISSN - 0146-0404

DOI - 10.1167/iovs.15-18924

Subject(s) - optical coherence tomography , segmentation , computer science , artificial intelligence , robustness (evolution) , leverage (statistics) , retinal , image segmentation , context (archaeology) , graph , cut , choroid , computer vision , pattern recognition (psychology) , medicine , retina , ophthalmology , optics , physics , paleontology , biochemistry , chemistry , biology , gene , theoretical computer science

Longitudinal imaging is becoming more commonplace for studies of disease progression, response to treatment, and healthy maturation. Accurate and reproducible quantification methods are desirable to fully mine the wealth of data in such datasets. However, most current retinal OCT segmentation methods are cross-sectional and fail to leverage the inherent context present in longitudinal sequences of images.

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