Longitudinal deep network for consistent OCT layer segmentation

Yufan He, Aaron Carass, Yihao Liu, Peter A. Calabresi, Shiv Saidha, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review

Abstract

Retinal layer thickness is an important bio-marker for people with multiple sclerosis (PwMS). In clinical practice, retinal layer thickness changes in optical coherence tomography (OCT) are widely used for monitoring multiple sclerosis (MS) progression. Recent developments in automated retinal layer segmentation algorithms allow cohort-level retina thinning to be observed in a large study of PwMS. However, variability in these results make it difficult to identify patient-level trends; this prevents patient specific disease monitoring and treatment planning using OCT. Deep learning based retinal layer segmentation algorithms have achieved state-of-the-art accuracy, but the segmentation is performed on each individual scan without utilizing longitudinal information, which can be important in reducing segmentation error and reveal subtle changes in retinal layers. In this paper, we propose a longitudinal OCT segmentation network which achieves more accurate and consistent layer thickness measurements for PwMS.

Original languageEnglish (US)
Pages (from-to)1874-1893
Number of pages20
JournalBiomedical Optics Express
Volume14
Issue number5
DOIs
StatePublished - May 1 2023
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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