Pseudo-real-time retinal layer segmentation for high-resolution adaptive optics optical coherence tomography

Worawee Janpongsri, Joey Huang, Ringo Ng, Daniel J. Wahl, Marinko V. Sarunic, Yifan Jian

Research output: Contribution to journalArticlepeer-review


We present a pseudo-real-time retinal layer segmentation for high-resolution Sensorless Adaptive Optics-Optical Coherence Tomography (SAO-OCT). Our pseudo-real-time segmentation method is based on Dijkstra's algorithm that uses the intensity of pixels and the vertical gradient of the image to find the minimum cost in a geometric graph formulation within a limited search region. It segments six retinal layer boundaries in an iterative process according to their order of prominence. The segmentation time is strongly correlated to the number of retinal layers to be segmented. Our program permits en face images to be extracted during data acquisition to guide the depth specific focus control and depth dependent aberration correction for high-resolution SAO-OCT systems. The average processing times for our entire pipeline for segmenting six layers in a retinal B-scan of 496 × 400 and 240 × 400 pixels are around 25.60 and 13.76 ms, respectively. When reducing the number of layers segmented to only two layers, the time required for a 240 × 400 pixel image is 8.26 ms.

Original languageEnglish (US)
Article numbere202000042
JournalJournal of Biophotonics
Issue number8
StatePublished - Aug 1 2020


  • graph search
  • image processing
  • pseudo-real-time
  • retinal layer segmentation

ASJC Scopus subject areas

  • General Chemistry
  • General Materials Science
  • General Biochemistry, Genetics and Molecular Biology
  • General Engineering
  • General Physics and Astronomy


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