A multi-modal dental dataset for semi-supervised deep learning image segmentation
Abstract In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Addition...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04306-9 |
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| author | Yaqi Wang Fan Ye Yifei Chen Chengkai Wang Chengyu Wu Feng Xu Zhean Ma Yi Liu Yifan Zhang Mingguo Cao Xiaodiao Chen |
| author_facet | Yaqi Wang Fan Ye Yifei Chen Chengkai Wang Chengyu Wu Feng Xu Zhean Ma Yi Liu Yifan Zhang Mingguo Cao Xiaodiao Chen |
| author_sort | Yaqi Wang |
| collection | DOAJ |
| description | Abstract In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. However, the scarcity of publicly available PXI and CBCT datasets hampers their use in tooth segmentation tasks. Therefore, this paper presents a multimodal dataset for Semi-supervised Tooth Segmentation (STS-Tooth) in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. STS-2D-Tooth includes 4,000 images and 900 masks, categorized by age into children and adults. Moreover, we have collected CBCTs providing more detailed and three-dimensional information, resulting in the STS-3D-Tooth dataset comprising 148,400 unlabeled scans and 8,800 masks. To our knowledge, this is the first multimodal dataset combining dental PXI and CBCT, and it is the largest tooth segmentation dataset, a significant step forward for the advancement of tooth segmentation. |
| format | Article |
| id | doaj-art-84d7407b48524ecf96e1a93ebce8f8b3 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-84d7407b48524ecf96e1a93ebce8f8b32025-01-26T12:14:46ZengNature PortfolioScientific Data2052-44632025-01-011211910.1038/s41597-024-04306-9A multi-modal dental dataset for semi-supervised deep learning image segmentationYaqi Wang0Fan Ye1Yifei Chen2Chengkai Wang3Chengyu Wu4Feng Xu5Zhean Ma6Yi Liu7Yifan Zhang8Mingguo Cao9Xiaodiao Chen10College of Media Engineering, Communication University of ZhejiangSchool of Computer Science, Hangzhou Dianzi UniversityHDU-ITMO Joint Institute, Hangzhou Dianzi UniversitySchool of Management, Hangzhou Dianzi UniversityDepartment of Mechanical, Electrical and Information Engineering, Shandong UniversitySchool of Computer Science, Hangzhou Dianzi UniversitySchool of Computer Science, Hangzhou Dianzi UniversityDepartment of Stomatology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaDepartment of Medicine, Lishui UniversityDepartment of Medicine, Lishui UniversitySchool of Computer Science, Hangzhou Dianzi UniversityAbstract In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. However, the scarcity of publicly available PXI and CBCT datasets hampers their use in tooth segmentation tasks. Therefore, this paper presents a multimodal dataset for Semi-supervised Tooth Segmentation (STS-Tooth) in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. STS-2D-Tooth includes 4,000 images and 900 masks, categorized by age into children and adults. Moreover, we have collected CBCTs providing more detailed and three-dimensional information, resulting in the STS-3D-Tooth dataset comprising 148,400 unlabeled scans and 8,800 masks. To our knowledge, this is the first multimodal dataset combining dental PXI and CBCT, and it is the largest tooth segmentation dataset, a significant step forward for the advancement of tooth segmentation.https://doi.org/10.1038/s41597-024-04306-9 |
| spellingShingle | Yaqi Wang Fan Ye Yifei Chen Chengkai Wang Chengyu Wu Feng Xu Zhean Ma Yi Liu Yifan Zhang Mingguo Cao Xiaodiao Chen A multi-modal dental dataset for semi-supervised deep learning image segmentation Scientific Data |
| title | A multi-modal dental dataset for semi-supervised deep learning image segmentation |
| title_full | A multi-modal dental dataset for semi-supervised deep learning image segmentation |
| title_fullStr | A multi-modal dental dataset for semi-supervised deep learning image segmentation |
| title_full_unstemmed | A multi-modal dental dataset for semi-supervised deep learning image segmentation |
| title_short | A multi-modal dental dataset for semi-supervised deep learning image segmentation |
| title_sort | multi modal dental dataset for semi supervised deep learning image segmentation |
| url | https://doi.org/10.1038/s41597-024-04306-9 |
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