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首页> 外文期刊>American Journal of Ophthalmology: The International Journal of Ophthalmology >Thickness profiles of retinal layers by optical coherence tomography image segmentation.
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Thickness profiles of retinal layers by optical coherence tomography image segmentation.

机译：视网膜相干厚度的光学相干断层扫描图像分割。

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摘要

PURPOSE: To report an image segmentation algorithm that was developed to provide quantitative thickness measurement of six retinal layers in optical coherence tomography (OCT) images. DESIGN: Prospective cross-sectional study. METHODS: Imaging was performed with time- and spectral-domain OCT instruments in 15 and 10 normal healthy subjects, respectively. A dedicated software algorithm was developed for boundary detection based on a 2-dimensional edge detection scheme, enhancing edges along the retinal depth while suppressing speckle noise. Automated boundary detection and quantitative thickness measurements derived by the algorithm were compared with measurements obtained from boundaries manually marked by three observers. Thickness profiles for six retinal layers were generated in normal subjects. RESULTS: The algorithm identified seven boundaries and measured thickness of six retinal layers: nerve fiber layer, inner plexiform layer and ganglion cell layer, inner nuclear layer, outer plexiform layer, outer nuclear layer and photoreceptor inner segments (ONL+PIS), and photoreceptor outer segments (POS). The root mean squared error between the manual and automatic boundary detection ranged between 4 and 9 mum. The mean absolute values of differences between automated and manual thickness measurements were between 3 and 4 mum, and comparable to interobserver differences. Inner retinal thickness profiles demonstrated minimum thickness at the fovea, corresponding to normal anatomy. The OPL and ONL+PIS thickness profiles respectively displayed a minimum and maximum thickness at the fovea. The POS thickness profile was relatively constant along the scan through the fovea. CONCLUSIONS: The application of this image segmentation technique is promising for investigating thickness changes of retinal layers attributable to disease progression and therapeutic intervention.
机译：用途：报告一种图像分割算法，该算法被开发用于在光学相干断层扫描（OCT）图像中提供六个视网膜层的定量厚度测量。设计：前瞻性横断面研究。方法：分别在15位和10位正常健康受试者中使用时域和频谱域OCT仪器进行成像。开发了一种专用的软件算法，用于基于二维边缘检测方案的边界检测，在抑制斑点噪声的同时增强了沿视网膜深度的边缘。通过算法得出的自动边界检测和定量厚度测量值与从由三个观察员手动标记的边界获得的测量值进行了比较。在正常受试者中产生了六个视网膜层的厚度分布图。结果：该算法确定了六个视网膜层的七个边界并测量了厚度：神经纤维层，内丛状层和神经节细胞层，内核层，外丛状层，外核层和感光器内部节段（ONL + PIS）以及感光器外段（POS）。手动和自动边界检测之间的均方根误差在4到9微米之间。自动和手动厚度测量之间的差异的平均绝对值在3-4毫米之间，与观察者之间的差异相当。视网膜内部厚度分布显示出中央凹处的最小厚度，与正常解剖结构相对应。 OPL和ONL + PIS厚度轮廓分别显示了中央凹处的最小和最大厚度。沿着整个中央凹的扫描，POS厚度分布相对恒定。结论：这种图像分割技术的应用有望用于调查可归因于疾病进展和治疗干预的视网膜层厚度变化。

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