Automated volumetric segmentation of retinal fluid on optical coherence tomography

Jie Wang, Miao Zhang, Alex D. Pechauer, Liang Liu, Thomas S. Hwang, David J. Wilson, Dengwang Li, Yali Jia

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation.

Original languageEnglish (US)
Pages (from-to)1577-1589
Number of pages13
JournalBiomedical Optics Express
Issue number4
StatePublished - Apr 30 2016

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics


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