Children's dental panoramic radiographs dataset for caries segmentation and dental disease detection.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
14 06 2023
14 06 2023
Historique:
received:
12
12
2022
accepted:
15
05
2023
medline:
16
6
2023
pubmed:
15
6
2023
entrez:
14
6
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37316638
doi: 10.1038/s41597-023-02237-5
pii: 10.1038/s41597-023-02237-5
pmc: PMC10267170
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
380Subventions
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : No. 62206242
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : No.2019YFC0118404
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2023. The Author(s).
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