Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
J Biophotonics. 2020 May;13(5):e201960187. doi: 10.1002/jbio.201960187. Epub 2020 Feb 20.
For spectral-domain optical coherence tomography (SD-OCT) studies of neurodegeneration, it is important to understand how segmentation algorithms differ in retinal layer thickness measurements, segmentation error locations and the impact of manual correction. Using macular SD-OCT images of frontotemporal degeneration patients and controls, we compare the individual and aggregate retinal layer thickness measurements provided by two commonly used algorithms, the Iowa Reference Algorithm and Heidelberg Spectralis, with manual correction of significant segmentation errors. We demonstrate small differences of most retinal layer thickness measurements between these algorithms. Outer sectors of the Early Treatment Diabetic Retinopathy Study grid require a greater percent of eyes to be corrected than inner sectors of the retinal nerve fiber layer (RNFL). Manual corrections affect thickness measurements mildly, resulting in at most a 5% change in RNFL thickness. Our findings can inform researchers how to best use different segmentation algorithms when comparing retinal layer thicknesses.
对于神经退行性疾病的光谱域光相干断层扫描（SD-OCT）研究，了解分割算法在视网膜层厚度测量、分割错误位置以及手动校正方面的差异非常重要。使用额颞叶变性患者和对照组的黄斑 SD-OCT 图像，我们比较了两种常用算法（爱荷华参考算法和海德堡 Spectralis）以及手动校正明显分割错误提供的个体和综合视网膜层厚度测量值。我们证明了这些算法之间大多数视网膜层厚度测量值的差异很小。早期糖尿病视网膜病变研究网格的外区比视网膜神经纤维层（RNFL）的内区需要更多百分比的眼睛进行校正。手动校正对厚度测量的影响较小，导致 RNFL 厚度最多变化 5%。我们的研究结果可以为研究人员提供信息，告知他们在比较视网膜层厚度时如何最好地使用不同的分割算法。
