论文标题
伪实时的视网膜层分割，用于高分辨率自适应光学光学相干断层扫描
Pseudo-real-time retinal layer segmentation for high-resolution adaptive optics optical coherence tomography
论文作者
论文摘要
我们提出了用于高分辨率传感器自适应光学光学相干断层扫描（SAO-OCT）的伪实时视网膜分割。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在有限的搜索区域内找到几何图公式中的最低成本。它根据其突出的顺序在迭代过程中段划分六个视网膜层边界。分割时间与要分割的视网膜层的数量密切相关。我们的程序允许在数据采集期间提取面部图像，以指导深度焦点控制和深度依赖性的差异校正校正高分辨率SAO-OCT系统。我们整个管道的平均处理时间分别在496x400像素和240x400像素的视网膜B扫描中分割六层分别为25.60 ms和13.76毫秒。当将分段的层数减少到仅两个层时，240x400像素图像所需的时间为8.26 ms。
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.
