Periorbital segmentation and distance prediction using deep learning allows for the objective quantification of disease state, treatment monitoring, and remote medicine. However, there are currently no reports of segmentation datasets for the purposes of training deep learning models with sub mm accuracy on the regions around the eyes. All images (n=2842) had the iris, sclera, lid, caruncle, and brow segmented by five trained annotators. Here, we validate this dataset through intra and intergrader reliability tests and show the utility of the data in training periorbital segmentation networks. All the annotations are publicly available for free download. Having access to segmentation datasets designed specifically for oculoplastic surgery will permit more rapid development of clinically useful segmentation networks which can be leveraged for periorbital distance prediction and disease classification. In addition to the annotations, we also provide an open-source toolkit for periorbital distance prediction from segmentation masks. The weights of all models have also been open-sourced and are publicly available for use by the community.

翻译：利用深度学习进行眼周区域分割与距离预测，能够实现疾病状态的客观量化、治疗监测以及远程医疗。然而，目前尚无专门用于训练深度学习模型、对眼部周围区域实现亚毫米级精度分割的数据集报告。所有图像（n=2842）均由五名经过培训的标注员对虹膜、巩膜、眼睑、泪阜及眉毛区域进行了分割标注。本文通过组内与组间标注者可靠性测试验证了该数据集的有效性，并展示了该数据在训练眼周区域分割网络中的实用性。所有标注数据均已公开，可供免费下载。获取专为眼整形外科设计的分割数据集，将有助于加速开发具有临床实用价值的分割网络，这些网络可进一步应用于眼周距离预测与疾病分类。除标注数据外，我们还提供了一套开源工具包，用于从分割掩码中预测眼周距离。所有模型的权重也已开源，可供研究社区公开使用。