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.

本研究解决了目前缺乏高精度眼周区域分割数据集的问题，提出了一套包括2842幅图像的开源数据集，在深度学习模型训练中具有重要价值。通过验证数据集的可靠性，研究表明其在眼科手术和疾病分类中具有广泛的应用潜力，助力临床相关的分割网络的快速发展。

眼科应用的开源眼眶周围分割数据集