Layer boundary evolution method for macular OCT layer segmentation

Yihao Liu, Aaron Carass, Yufan He, Bhavna J. Antony, Angeliki Filippatou, Shiv Saidha, Sharon Solomon, Peter Calabresi, Jerry Ladd Prince

Research output: Contribution to journalArticle

Abstract

Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra-and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.

Original languageEnglish (US)
Article number349227
Pages (from-to)1064-1080
Number of pages17
JournalBiomedical Optics Express
Volume10
Issue number3
DOIs
StatePublished - Mar 1 2019

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ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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