Validation of automated artificial intelligence segmentation of optical coherence tomography images

PurposeTo benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera.MethodsA convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results.ResultsThe CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders’ compartmentalization was higher than the mean score for intra-grader group comparison.ConclusionThe proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders.

研究目的：本数据集旨在针对玻璃体、视网膜、脉络膜、巩膜等眼后部腔室的图像分割任务（即逐像素分类），开展光谱域（Spectral-Domain, SD）与扫频源（Swept-Source, SS）光学相干断层扫描（Optical Coherence Tomography, OCT）图像分割性能的基准测试，对比人类与机器的表现。
研究方法：本研究使用由资深视网膜专科医师（金标准标注者）标注的OCT B扫描图像训练卷积神经网络（Convolutional Neural Network, CNN），以实现眼后部腔室的图像分割。独立基准数据集包含30幅SD-OCT与30幅SS-OCT图像，由三类眼科专业水平各异的评分者完成手动分割。另有9名评分者在三轮连续评估中完成额外60幅图像的标注工作。本研究测量了评分者间与评分者内的一致性，并将其与卷积神经网络的分割结果进行对比分析。
研究结果：卷积神经网络的训练数据集总计包含6210幅手动分割图像，源自2070幅B扫描图像（其中1046幅SD-OCT、1024幅SS-OCT，以及630幅C扫描）。卷积神经网络的分割结果与所有评分者组均具有较高一致性。针对所有腔室与评分者组，卷积神经网络分割结果与各评分者组分割结果的平均交并比（Intersection over Union, IOU）得分，均高于评分者组内比较的平均得分。
研究结论：本研究提出的用于OCT B扫描（SD-OCT与SS-OCT）中眼后部腔室自动分割的深度学习分割算法（卷积神经网络），其性能可与人类评分者的手动分割结果相媲美。