PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs
Abstract Objective PXseg, a novel approach for tooth segmentation, numbering and abnormal morphology detection in panoramic X-ray (PX), was designed and promoted through optimizing annotation and applying pre-training. Methods Derived from multicenter, ctPXs generated from cone beam computed tomogra...
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BMC
2025-07-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-06356-w |
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| author | Raokaijuan Wang Fangyuan Cheng Guangsheng Dai Jiayu Zhang Chengmin Fan Jinghong Yu Juan Li Fulin Jiang |
| author_facet | Raokaijuan Wang Fangyuan Cheng Guangsheng Dai Jiayu Zhang Chengmin Fan Jinghong Yu Juan Li Fulin Jiang |
| author_sort | Raokaijuan Wang |
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| description | Abstract Objective PXseg, a novel approach for tooth segmentation, numbering and abnormal morphology detection in panoramic X-ray (PX), was designed and promoted through optimizing annotation and applying pre-training. Methods Derived from multicenter, ctPXs generated from cone beam computed tomography (CBCT) with accurate 3D labels were utilized for pre-training, while conventional PXs (cPXs) with 2D labels were input for training. Visual and statistical analyses were conducted using the internal dataset to assess segmentation and numbering performances of PXseg and compared with the model without ctPX pre-training, while the accuracy of PXseg detecting abnormal teeth was evaluated using the external dataset consisting of cPXs with complex dental diseases. Besides, a diagnostic testing was performed to contrast diagnostic efficiency with and without PXseg’s assistance. Results The DSC and F1-score of PXseg in tooth segmentation reached 0.882 and 0.902, which increased by 4.6% and 4.0% compared to the model without pre-training. For tooth numbering, the F1-score of PXseg reached 0.943 and increased by 2.2%. Based on the promotion in segmentation, the accuracy of abnormal tooth morphology detection exceeded 0.957 and was 4.3% higher. A website was constructed to assist in PX interpretation, and the diagnostic efficiency was greatly enhanced with the assistance of PXseg. Conclusions The application of accurate labels in ctPX increased the pre-training weight of PXseg and improved the training effect, achieving promotions in tooth segmentation, numbering and abnormal morphology detection. Rapid and accurate results provided by PXseg streamlined the workflow of PX diagnosis, possessing significant clinical application prospect. |
| format | Article |
| id | doaj-art-c54e0a46e4d14ee29e152b7c3ec96360 |
| institution | Kabale University |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-c54e0a46e4d14ee29e152b7c3ec963602025-08-20T03:42:03ZengBMCBMC Oral Health1472-68312025-07-0125111210.1186/s12903-025-06356-wPXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographsRaokaijuan Wang0Fangyuan Cheng1Guangsheng Dai2Jiayu Zhang3Chengmin Fan4Jinghong Yu5Juan Li6Fulin Jiang7Department of Orthodontics, West China School of Stomatology, Sichuan UniversityChengdu Boltzmann Intelligence Technology Co., LtdChengdu Boltzmann Intelligence Technology Co., LtdChengdu Boltzmann Intelligence Technology Co., LtdChengdu Boltzmann Intelligence Technology Co., LtdChongqing University Three Gorges HospitalDepartment of Orthodontics, West China School of Stomatology, Sichuan UniversityDepartment of Orthodontics, Chongqing University Three Gorges HospitalAbstract Objective PXseg, a novel approach for tooth segmentation, numbering and abnormal morphology detection in panoramic X-ray (PX), was designed and promoted through optimizing annotation and applying pre-training. Methods Derived from multicenter, ctPXs generated from cone beam computed tomography (CBCT) with accurate 3D labels were utilized for pre-training, while conventional PXs (cPXs) with 2D labels were input for training. Visual and statistical analyses were conducted using the internal dataset to assess segmentation and numbering performances of PXseg and compared with the model without ctPX pre-training, while the accuracy of PXseg detecting abnormal teeth was evaluated using the external dataset consisting of cPXs with complex dental diseases. Besides, a diagnostic testing was performed to contrast diagnostic efficiency with and without PXseg’s assistance. Results The DSC and F1-score of PXseg in tooth segmentation reached 0.882 and 0.902, which increased by 4.6% and 4.0% compared to the model without pre-training. For tooth numbering, the F1-score of PXseg reached 0.943 and increased by 2.2%. Based on the promotion in segmentation, the accuracy of abnormal tooth morphology detection exceeded 0.957 and was 4.3% higher. A website was constructed to assist in PX interpretation, and the diagnostic efficiency was greatly enhanced with the assistance of PXseg. Conclusions The application of accurate labels in ctPX increased the pre-training weight of PXseg and improved the training effect, achieving promotions in tooth segmentation, numbering and abnormal morphology detection. Rapid and accurate results provided by PXseg streamlined the workflow of PX diagnosis, possessing significant clinical application prospect.https://doi.org/10.1186/s12903-025-06356-wTooth segmentationPanoramic X-rayCone beam computed tomographyConvolutional neural network |
| spellingShingle | Raokaijuan Wang Fangyuan Cheng Guangsheng Dai Jiayu Zhang Chengmin Fan Jinghong Yu Juan Li Fulin Jiang PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs BMC Oral Health Tooth segmentation Panoramic X-ray Cone beam computed tomography Convolutional neural network |
| title | PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs |
| title_full | PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs |
| title_fullStr | PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs |
| title_full_unstemmed | PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs |
| title_short | PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs |
| title_sort | pxseg automatic tooth segmentation numbering and abnormal morphology detection based on cbct and panoramic radiographs |
| topic | Tooth segmentation Panoramic X-ray Cone beam computed tomography Convolutional neural network |
| url | https://doi.org/10.1186/s12903-025-06356-w |
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