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Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection
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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267170/ https://www.ncbi.nlm.nih.gov/pubmed/37316638 http://dx.doi.org/10.1038/s41597-023-02237-5 |
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author | Zhang, Yifan Ye, Fan Chen, Lingxiao Xu, Feng Chen, Xiaodiao Wu, Hongkun Cao, Mingguo Li, Yunxiang Wang, Yaqi Huang, Xingru |
author_facet | Zhang, Yifan Ye, Fan Chen, Lingxiao Xu, Feng Chen, Xiaodiao Wu, Hongkun Cao, Mingguo Li, Yunxiang Wang, Yaqi Huang, Xingru |
author_sort | Zhang, Yifan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10267170 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102671702023-06-15 Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection Zhang, Yifan Ye, Fan Chen, Lingxiao Xu, Feng Chen, Xiaodiao Wu, Hongkun Cao, Mingguo Li, Yunxiang Wang, Yaqi Huang, Xingru Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-06-14 /pmc/articles/PMC10267170/ /pubmed/37316638 http://dx.doi.org/10.1038/s41597-023-02237-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Zhang, Yifan Ye, Fan Chen, Lingxiao Xu, Feng Chen, Xiaodiao Wu, Hongkun Cao, Mingguo Li, Yunxiang Wang, Yaqi Huang, Xingru Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection |
title | Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection |
title_full | Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection |
title_fullStr | Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection |
title_full_unstemmed | Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection |
title_short | Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection |
title_sort | children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267170/ https://www.ncbi.nlm.nih.gov/pubmed/37316638 http://dx.doi.org/10.1038/s41597-023-02237-5 |
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