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脉络膜血管密度测量算法的定性比较。

Qualitative comparison of choroidal vascularity measurement algorithms.

机构信息

Smt. Kanuri Santhamma Centre for Vitreo-Retinal Diseases, L V Prasad Eye Institute, Hyderabad, Telangana, India.

出版信息

Indian J Ophthalmol. 2018 Dec;66(12):1785-1789. doi: 10.4103/ijo.IJO_663_18.

Abstract

PURPOSE

To compare the accuracy of manual and automated binarization technique for the analysis of choroidal vasculature.

METHODS

This retrospective study was performed on a total of 98 eyes of 60 healthy subjects. Fovea-centered swept source optical coherence tomography (SS-OCT) scans were obtained and choroidal area was binarized using manual and automated image binarization technique separately. Choroidal vessel visualization in the binarized scans were subjectively graded (grades 0-100) by comparing them with the original OCT scan images by two masked graders. The subjective variability and repeatability was compared between two binarization method groups. Intergrader and intragrader variability was estimated using paired t-test. The degree of agreement between the grades for each observer and between the observers was evaluated using Bland-Altman plot.

RESULTS

The mean accuracy grades of the automatically binarized images were significantly (P < 0.001) higher (93.38% ± 1.70%) than that of manually binarized images (78.06% ± 2.92%). There was a statistically significant variability and poor agreement between the mean interobserver grades in the manual binarization arm.

CONCLUSION

Automated image binarization technique is faster and appears to be more accurate in comparison to the manual method.

摘要

目的

比较手动和自动二值化技术分析脉络膜血管的准确性。

方法

本回顾性研究共纳入 60 名健康受试者的 98 只眼。获取以黄斑为中心的扫频源光学相干断层扫描(SS-OCT)图像,并分别使用手动和自动图像二值化技术对脉络膜区域进行二值化。由两名掩蔽评分者通过比较与原始 OCT 扫描图像,对二值化扫描图像中的脉络膜血管可视化进行主观分级(0-100 级)。比较两种二值化方法组之间的主观可变性和可重复性。使用配对 t 检验评估组内和组间的变异性。使用 Bland-Altman 图评估每位观察者的分级之间以及观察者之间的一致性程度。

结果

自动二值化图像的平均准确度等级明显(P<0.001)更高(93.38%±1.70%),而手动二值化图像的平均准确度等级(78.06%±2.92%)较低。在手动二值化臂中,观察者之间的平均分级存在统计学上显著的可变性和较差的一致性。

结论

与手动方法相比,自动图像二值化技术更快,并且似乎更准确。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93a4/6256898/adf4631c2378/IJO-66-1785-g001.jpg

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