Glaucomatous damage can be quantified by measuring the thickness of different retinal layers. However, poor image quality may hamper the accuracy of the layer thickness measurement. We determined the effect of poor image quality (low signal-to-noise ratio) on the different layer thicknesses and compared different segmentation algorithms regarding their robustness against this degrading effect. For this purpose, we performed OCT measurements in the macular area of healthy subjects and degraded the image quality by employing neutral density filters. We also analysed OCT scans from glaucoma patients with different disease severity. The algorithms used were: The Canon HS-100’s built-in algorithm, DOCTRAP, IOWA, and FWHM, an approach we developed. We showed that the four algorithms used were all susceptible to noise at a varying degree, depending on the retinal layer assessed, and the results between different algorithms were not interchangeable. The algorithms also differed in their ability to differentiate between young healthy eyes and older glaucoma eyes and failed to accurately separate different glaucoma stages from each other.

中文翻译：

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在不同信噪比条件下从光学相干断层扫描中使用不同的分割算法检索的视网膜层厚度

青光眼损伤可以通过测量不同视网膜层的厚度来量化。但是，较差的图像质量可能会影响层厚度测量的准确性。我们确定了不良图像质量（低信噪比）对不同层厚度的影响，并比较了不同的分割算法的鲁棒性对这种降级效果的影响。为此，我们在健康受试者的黄斑区域进行了OCT测量，并通过使用中性密度滤镜降低了图像质量。我们还分析了患有不同疾病严重程度的青光眼患者的OCT扫描。使用的算法为：佳能HS-100的内置算法，DOCTRAP，IOWA和FWHM，这是我们开发的方法。我们表明，所使用的四种算法在不同程度上都容易受到噪声的影响，取决于评估的视网膜层，并且不同算法之间的结果不可互换。该算法在区分年轻健康眼和老年青光眼的能力上也有所不同，并且未能准确区分不同的青光眼阶段。