  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.
