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Quantifying disrupted outer retinal-subretinal layer in SD-OCT images in choroidal neovascularization

机译:量化脉络膜新生血管中SD-OCT图像中受损的视网膜外视网膜下层

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Purpose. We reported a fully automated method to identify and quantify the thickness of the outer retinal-subretinal (ORSR) layer from clinical spectral-domain optical coherence tomography (SD-OCT) scans of choroidal neovascularization (CNV) due to exudative age-related macular degeneration (eAMD). Methods. A total of 23 subjects with CNV met eligibility. Volumetric SD-OCT scans of 23 eyes were obtained (Zeiss Cirrus, 200 × 200 × 1024 voxels). In a subset of eyes, scans were repeated. The OCT volumes were analyzed using our standard parameters and using a 3-dimensional (3D) graph-search approach with an adaptive cost function. A retinal specialist graded the segmentation as generally accurate, local segmentation inaccuracies, or failure. Reproducibility on repeat scans was analyzed using root mean square coefficient of variation (RMS CV) of the average ORSR thickness. Results. Using a standard segmentation approach, 1/23 OCT segmentations was graded generally accurate and 22/23 were failure(s). With the adaptive method 21/23 segmentations were graded generally accurate; 2/23 were local segmentation inaccuracies and none was a failure. The intermethod quality of segmentation was significantly different (P 0.001). The average ORSR thickness measured on CNV patients (78.0 μm; 95% confidence interval [CI], 72.5-83.4 μm) is significantly larger (P 0.001) than normal average ORSR layer thickness (51.5 ± 3.3 μm). The RMS CV was 8.1%. Conclusions. We have developed a fully automated 3D method for segmenting the ORSR layer in SD-OCT of patients with CNV from eAMD. Our method can quantify the ORSR layer thickness in the presence of fluid, which has the potential to augment management accuracy and efficiency of anti-VEGF treatment.
机译:目的。我们报告了一种全自动方法,该方法可通过脉络膜新生血管形成(CNV)的临床光谱域光学相干断层扫描(SD-OCT)扫描来识别和量化视网膜下视网膜下(ORSR)层的厚度,该扫描是由于渗出性年龄相关性黄斑变性(eAMD)。方法。共有23名CNV受试者符合资格。进行了23只眼睛的SD-OCT体积扫描(Zeiss Cirrus,200×200×1024体素)。在眼睛的子集中,重复扫描。使用我们的标准参数并使用具有自适应成本函数的3维(3D)图形搜索方法来分析OCT量。视网膜专家将分割结果分级为一般准确,局部分割错误或失败。使用平均ORSR厚度的均方根变异系数(RMS CV)分析重复扫描的可重复性。结果。使用标准的细分方法,将1/23个OCT细分分级为大致准确,而22/23则为失败。采用自适应方法,将21/23的细分划分为大致准确的等级; 2/23是局部分割不准确,没有一个失败。分割的方法间质量有显着差异(P 0.001)。在CNV患者上测得的平均ORSR厚度(78.0μm; 95%置信区间[CI],72.5-83.4μm)明显大于正常的平均ORSR层厚度(51.5±3.3μm)(P 0.001)。 RMS CV为8.1%。结论。我们已经开发了一种全自动3D方法,用于分割eAMD CNV患者的SD-OCT中的ORSR层。我们的方法可以在存在流体的情况下量化ORSR层的厚度,这可能会提高管理准确性和抗VEGF治疗的效率。

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