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.

中文翻译：

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用于高分辨率自适应光学光学相干断层扫描的伪实时视网膜层分割。

我们为高分辨率的无传感器自适应光学光学相干断层扫描（SAO-OCT）提出了伪实时视网膜层分割。我们的伪实时分割方法基于Dijkstra的算法，该算法使用像素的强度和图像的垂直梯度来在有限的搜索区域内找到几何图形公式中的最小成本。它根据其突出顺序在迭代过程中分割六个视网膜层边界。分割时间与要分割的视网膜层数密切相关。我们的计划允许面对面数据采集​​期间要提取的图像，以指导高分辨率SAO-OCT系统的深度特定聚焦控制和深度相关像差校正。我们整个流水线在视网膜B扫描中分割496×400和240×400像素的六层图像的平均处理时间分别约为25.60和13.76 ms。将分割的层数减少到仅两层时，240×400像素图像所需的时间为8.26 ms。