School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK.
King's College Hospital NHS Foundation Trust, London, UK.
Transl Vis Sci Technol. 2021 Jan 4;10(1):27. doi: 10.1167/tvst.10.1.27.
To evaluate the performance of the Pegasus-OCT (Visulytix Ltd) multiclass automated fluid segmentation algorithms on independent spectral domain optical coherence tomography data sets.
The Pegasus automated fluid segmentation algorithms were applied to three data sets with edematous pathology, comprising 750, 600, and 110 b-scans, respectively. Intraretinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelial detachment (PED) were automatically segmented by Pegasus-OCT for each b-scan where ground truth from data set owners was available. Detection performance was assessed by calculating sensitivities and specificities, while Dice coefficients were used to assess agreement between the segmentation methods.
For two data sets, IRF detection yielded promising sensitivities (0.98 and 0.94, respectively) and specificities (1.00 and 0.98) but less consistent agreement with the ground truth (dice coefficients 0.81 and 0.59); likewise, SRF detection showed high sensitivity (0.86 and 0.98) and specificity (0.83 and 0.89) but less consistent agreement (0.59 and 0.78). PED detection on the first data set showed moderate agreement (0.66) with high sensitivity (0.97) and specificity (0.98). IRF detection in a third data set yielded less favorable agreement (0.46-0.57) and sensitivity (0.59-0.68), attributed to image quality and ground truth grader discordance.
The Pegasus automated fluid segmentation algorithms were able to detect IRF, SRF, and PED in SD-OCT b-scans acquired across multiple independent data sets. Dice coefficients and sensitivity and specificity values indicate the potential for application to automated detection and monitoring of retinal diseases such as age-related macular degeneration and diabetic macular edema.
The potential of Pegasus-OCT for automated fluid quantification and differentiation of IRF, SRF, and PED in OCT images has application to both clinical practice and research.
评估 Pegasus-OCT（Visulytix Ltd）多类别自动流体分割算法在独立的谱域光相干断层扫描数据集上的性能。
将 Pegasus 自动流体分割算法应用于三个包含水肿性病变的数据集，每个数据集分别包含 750、600 和 110 个 B 扫描。在可获得数据集所有者提供的真实数据的情况下，Pegasus-OCT 自动对每个 B 扫描的视网膜内液（IRF）、视网膜下液（SRF）和色素上皮脱离（PED）进行分割。通过计算敏感性和特异性来评估检测性能，而 Dice 系数用于评估分割方法之间的一致性。
对于两个数据集，IRF 检测的敏感性分别为 0.98 和 0.94，特异性分别为 1.00 和 0.98，但与真实数据的一致性不一致（Dice 系数分别为 0.81 和 0.59）；同样，SRF 检测的敏感性分别为 0.86 和 0.98，特异性分别为 0.83 和 0.89，但一致性不一致（Dice 系数分别为 0.59 和 0.78）。第一个数据集的 PED 检测与高敏感性（0.97）和特异性（0.98）具有中等一致性（0.66）。第三个数据集的 IRF 检测得到的一致性较差（0.46-0.57）和敏感性（0.59-0.68），这归因于图像质量和真实数据评估员的不一致。
Pegasus 自动流体分割算法能够在多个独立数据集采集的 SD-OCT B 扫描中检测 IRF、SRF 和 PED。Dice 系数以及敏感性和特异性值表明，该算法有可能应用于自动检测和监测年龄相关性黄斑变性和糖尿病性黄斑水肿等视网膜疾病。
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