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基于自动谱域光学相干断层扫描图像分割的黄斑脉络膜厚度测量的验证

Validation of Macular Choroidal Thickness Measurements from Automated SD-OCT Image Segmentation.

作者信息

Twa Michael D, Schulle Krystal L, Chiu Stephanie J, Farsiu Sina, Berntsen David A

机构信息

*OD, PhD, FAAO †OD, FAAO ‡PhD School of Optometry (MDT), Department of Biomedical Engineering (MDT), University of Alabama at Birmingham, Birmingham, Alabama; College of Optometry, University of Houston, Houston, Texas (KLS, DAB); Department of Biomedical Engineering, Duke University, Durham, North Carolina (SJC, SF); and Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina (SF).

出版信息

Optom Vis Sci. 2016 Nov;93(11):1387-1398. doi: 10.1097/OPX.0000000000000985.

Abstract

PURPOSE

Spectral domain optical coherence tomography (SD-OCT) imaging permits in vivo visualization of the choroid with micron-level resolution over wide areas and is of interest for studies of ocular growth and myopia control. We evaluated the speed, repeatability, and accuracy of a new image segmentation method to quantify choroid thickness compared to manual segmentation.

METHODS

Two macular volumetric scans (25 × 30°) were taken from 30 eyes of 30 young adult subjects in two sessions, 1 hour apart. A single rater manually delineated choroid thickness as the distance between Bruch's membrane and sclera across three B-scans (foveal, inferior, and superior-most scan locations). Manual segmentation was compared to an automated method based on graph theory, dynamic programming, and wavelet-based texture analysis. Segmentation performance comparisons included processing speed, choroid thickness measurements across the foveal horizontal midline, and measurement repeatability (95% limits of agreement (LoA)).

RESULTS

Subjects were healthy young adults (n = 30; 24 ± 2 years; mean ± SD; 63% female) with spherical equivalent refractive error of -3.46 ± 2.69D (range: +2.62 to -8.50D). Manual segmentation took 200 times longer than automated segmentation (780 vs. 4 seconds). Mean choroid thickness at the foveal center was 263 ± 24 μm (manual) and 259 ± 23 μm (automated), and this difference was not significant (p = 0.10). Regional segmentation errors across the foveal horizontal midline (±15°) were ≤9 μm (median) except for nasal-most regions closest to the nasal peripapillary margin-15 degrees (19 μm) and 12 degrees (16 μm) from the foveal center. Repeatability of choroidal thickness measurements had similar repeatability between segmentation methods (manual LoA: ±15 μm; automated LoA: ±14 μm).

CONCLUSIONS

Automated segmentation of SD-OCT data by graph theory and dynamic programming is a fast, accurate, and reliable method to delineate the choroid. This approach will facilitate longitudinal studies evaluating changes in choroid thickness in response to novel optical corrections and in ocular disease.

摘要

目的

光谱域光学相干断层扫描(SD-OCT)成像能够在体内以微米级分辨率对大面积脉络膜进行可视化,这对于眼部生长和近视控制的研究具有重要意义。我们评估了一种新的图像分割方法在量化脉络膜厚度方面的速度、可重复性和准确性,并与手动分割进行了比较。

方法

对30名年轻成年受试者的30只眼睛进行了两次黄斑区容积扫描(25×30°),两次扫描间隔1小时。由一名评估者手动划定脉络膜厚度,即跨越三次B扫描(中央凹、下方和最上方扫描位置)测量Bruch膜与巩膜之间的距离。将手动分割与基于图论、动态规划和基于小波的纹理分析的自动分割方法进行比较。分割性能比较包括处理速度、中央凹水平中线处的脉络膜厚度测量以及测量的可重复性(95%一致性界限(LoA))。

结果

受试者为健康年轻成年人(n = 30;24±2岁;平均值±标准差;63%为女性),等效球镜屈光不正为-3.46±2.69D(范围:+2.62至-8.50D)。手动分割比自动分割耗时长200倍(780秒对4秒)。中央凹中心处的平均脉络膜厚度,手动测量为263±24μm,自动测量为259±23μm,差异无统计学意义(p = 0.10)。除了最靠近鼻侧视乳头周围边缘且距中央凹中心15度(19μm)和12度(16μm)的鼻侧最远端区域外,中央凹水平中线(±15°)处的区域分割误差≤9μm(中位数)。脉络膜厚度测量的可重复性在两种分割方法之间相似(手动LoA:±15μm;自动LoA:±14μm)。

结论

通过图论和动态规划对SD-OCT数据进行自动分割是一种快速、准确且可靠的脉络膜描绘方法。这种方法将有助于进行纵向研究,以评估脉络膜厚度在新型光学矫正和眼部疾病影响下的变化。

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