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Estimation of papilledema severity using spectral-domain optical coherence tomography.

机译：使用光谱域光学相干断层扫描估计乳头状浮肿的严重程度。

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摘要

Papilledema is a particular type of optic disc swelling caused by elevated intracranial pressure. By observing the visible features from fundus images or direct funduscopic examination, a typical method of assessing papilledema (i.e., the six-stage Frisen grading system) is qualitative and frequently suffers from low reproducibility.;Compared to fundus images, spectral-domain optical coherence tomography (SD-OCT) is a relatively new imaging technique and enables the cross-sectional information of the retina to be acquired. Using SD-OCT images, quantitative measurements like evaluating the retinal volume or depth are intuitively more robust than the traditional qualitative approach to evaluate papilledema. Also, multiple studies suggest that the deformation of the peripapillary retinal pigment epithelium and/or Bruch's membrane (pRPE/BM) may reflect the intracranial pressure change. In other words, modeling/quantifying the pRPE/BM shape can potentially be another indicator of papilledema. However, when the optic disc is severely swollen, the retinal structure is dramatically deformed and often causes the commercial SD-OCT devices to fail to segment the retinal layers. Without appropriate layer segmentation, all the retinal measurements are not reliable.;To solve the current issue of inconsistently assessing papilledema severity, a comprehensive machine-learning framework is proposed in this doctoral work to achieve the goal by accomplishing following four aims. First, robust approaches are developed to automatically segment the retinal layers in 2D and 3D SD-OCT images, even though the optic discs can be severely swollen. Second, the semi- and fully automated methodologies are designed to segment the pRPE/BM opening under the swollen inner retina in these SD-OCT images. Third, the pRPE/BM shape models are constructed using both 2D and 3D SD-OCT images, and then the 2D/3D pRPE/BM shape measures are computed. Finally, based on the previously segmented retinal layers, eight OCT 2D/3D global/local measurements of retinal structure are reliably computed. Considering both the 2D/3D pRPE/BM shape measures and these eight OCT features as an input set, a machine-learning framework using the random forest technique is proposed to compute a papilledema severity score (PSS) on a continuous scale. The newly proposed PSS is expected to be an alternative to the traditional qualitative method to provide a more objective measurement of assessing papilledema severity.
机译：视乳头水肿是颅内压升高引起的一种特殊类型的视盘红肿。通过观察眼底图像或直接进行眼底镜检查的可见特征，评估乳头状水肿的典型方法（即六阶段弗里森分级系统）是定性的，并且经常具有低再现性。与眼底图像相比，光谱域光学相干性体层摄影术（SD-OCT）是一种相对较新的成像技术，可以获取视网膜的横截面信息。使用SD-OCT图像，诸如评估视网膜体积或深度之类的定量测量在直观上比评估乳头水肿的传统定性方法更可靠。此外，多项研究表明，乳头周围视网膜色素上皮和/或布鲁赫膜（pRPE / BM）的变形可能反映了颅内压的变化。换句话说，对pRPE / BM形状进行建模/量化可能是乳头水肿的另一个指标。但是，当视盘严重肿胀时，视网膜结构会急剧变形，并经常导致商用SD-OCT设备无法分割视网膜层。如果没有适当的层分割，所有的视网膜测量都是不可靠的。为了解决当前评估乳头水肿严重程度不一致的问题，本博士论文提出了一个全面的机器学习框架，以实现以下四个目标。首先，开发了可靠的方法来自动分割2D和3D SD-OCT图像中的视网膜层，即使视盘可能会严重肿胀。其次，设计了半自动和全自动方法来分割这些SD-OCT图像中肿胀的内部视网膜下方的pRPE / BM开口。第三，使用2D和3D SD-OCT图像构建pRPE / BM形状模型，然后计算2D / 3D pRPE / BM形状度量。最后，基于先前分割的视网膜层，可以可靠地计算出八个OCT 2D / 3D全局/局部视网膜结构测量值。考虑到2D / 3D pRPE / BM形状度量和这8个OCT特征作为输入集，提出了一种使用随机森林技术的机器学习框架来连续计算乳头水肿严重程度评分（PSS）。新提议的PSS有望替代传统的定性方法，以提供更客观的评估乳头水肿严重程度的方法。

著录项

  • 作者

    Wang, Jui-Kai.;

  • 作者单位

    The University of Iowa.;

  • 授予单位 The University of Iowa.;
  • 学科 Electrical engineering.;Biomedical engineering.;Ophthalmology.;Neurosciences.;Medical imaging.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 149 p.
  • 总页数 149
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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