公开首个眼周高精度分割数据集，助力眼科疾病量化分析
Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications
- 构建2842张眼周区域标注图像，涵盖虹膜、眼白等5个部位
- 通过多专家一致性验证，确保标注可靠性和模型训练质量
- 配套开源工具与模型权重，支持眼周距离预测与临床应用
基于深度学习的眼周分割与距离预测可实现疾病状态的客观量化、治疗监测和远程医疗。然而，目前尚无针对眼周区域亚毫米级精度分割的深度学习训练数据集报道。本研究收集了2842张图像，由五名受训标注员对虹膜、巩膜、眼睑、内眦赘皮和眉毛进行精细分割。通过组内与组间一致性检验验证了该数据集的可靠性，并展示了其在训练眼周分割网络中的有效性。所有标注数据均免费公开下载。该数据集专为眼整形外科设计，将加速临床可用分割网络的开发，用于眼周距离预测和疾病分类。除标注数据外，还提供开源工具包，可从分割掩码中进行眼周距离预测。所有模型权重也已开源，供社区自由使用。
原文摘要 · Abstract (English)
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.
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