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

TL;DR

This paper introduces a fast, pseudo-real-time segmentation method for retinal layers in high-resolution adaptive optics OCT, enabling real-time imaging guidance and correction during data acquisition.

Contribution

The novel segmentation approach uses Dijkstra's algorithm for rapid, accurate retinal layer boundary detection in high-resolution OCT images.

Findings

Segmentation times are approximately 25.60 ms for six layers in 496x400 images.

Processing time reduces to about 8.26 ms when segmenting only two layers.

The method enables en face image extraction during data acquisition for focus and aberration correction.

Abstract

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 496x400 pixels and 240x400 pixels are around 25.60 ms and 13.76 ms, respectively. When reducing the number of layers segmented to only two layers, the time required for a 240x400 pixel image is 8.26 ms.