黄斑OCT图像的视网膜层使用边界分类外文翻译资料

 2022-08-26 16:33:04

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黄斑OCT图像的视网膜层使用边界分类

分割

Andrew Lang，1,*Aaron Carass，1Matthew Hauser，1Elias S. Sotirchos，2Peter A. Calabresi，2Howard S. Ying，3和Jerry L. Prince1

Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA

2Department of Neurology, The Johns Hopkins School of Medicine,

Baltimore, MD 21287, USA

3Wilmer Eye Institute, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA

lowast;lang@jhu.edu

摘要：光学相干断层扫描（OCT）已被证明是眼科学必不可少的成像方式，并且在神经病学中被证明是非常重要的。OCT能够在视神经乳头和黄斑处视网膜进行高分辨率成像。黄斑视网膜层厚度提供了有用的诊断信息，并且已经显示与几种疾病中的疾病严重程度的测量很好地相关。由于这些层的手动分割是耗时的并且易于产生偏差，因此自动分割方法对于充分利用该技术是至关重要的。在这项工作中，我们建立了一个随机森林分类器，来分割由OCT所获得的黄斑立方体图像中的八个视网膜层。随机森林分类器学习各层之间的边界像素，为每个边界生成准确的概率图，然后对其进行处理以最终确定边界。使用该算法，我们可以精确地将黄斑立方体中包含的整个视网膜分割成九个边界，对于其中的任何一个至少精确度为4.3微米。对健康和多发性硬化患者进行了实验，两组之间的算法准确性没有差异。

copy;2013美国光学学会

OCIS代码：（100.0100）图像处理;（170.4470）眼科;（170.4500）光学相干断层扫描。

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