State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 310000, China.
Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, Sendai, 310000, Japan.
Sci Data. 2023 Jun 14;10(1):380. doi: 10.1038/s41597-023-02237-5.
When dentists see pediatric patients with more complex tooth development than adults during tooth replacement, they need to manually determine the patient's disease with the help of preoperative dental panoramic radiographs. To the best of our knowledge, there is no international public dataset for children's teeth and only a few datasets for adults' teeth, which limits the development of deep learning algorithms for segmenting teeth and automatically analyzing diseases. Therefore, we collected dental panoramic radiographs and cases from 106 pediatric patients aged 2 to 13 years old, and with the help of the efficient and intelligent interactive segmentation annotation software EISeg (Efficient Interactive Segmentation) and the image annotation software LabelMe. We propose the world's first dataset of children's dental panoramic radiographs for caries segmentation and dental disease detection by segmenting and detecting annotations. In addition, another 93 dental panoramic radiographs of pediatric patients, together with our three internationally published adult dental datasets with a total of 2,692 images, were collected and made into a segmentation dataset suitable for deep learning.
当牙医在替牙期看到比成年人牙齿发育更为复杂的儿科患者时，他们需要借助术前口腔全景片手动确定患者的疾病。据我们所知，目前还没有国际公共的儿童牙齿数据集，只有少数成人牙齿数据集，这限制了用于牙齿分割和自动分析疾病的深度学习算法的发展。因此，我们收集了来自 106 名 2 至 13 岁儿科患者的口腔全景片和病例，并借助高效智能的交互式分割标注软件 EISeg（Efficient Interactive Segmentation）和图像标注软件 LabelMe，提出了世界上第一个用于龋齿分割和口腔疾病检测的儿童口腔全景片数据集，通过分割和检测标注。此外，我们还收集了另外 93 张儿科患者的口腔全景片，并结合我们的三个已发布的国际成人牙齿数据集，总共 2692 张图像，制作成适合深度学习的分割数据集。
