Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography
Abstract Recent advancements in deep learning have revolutionized digital dentistry, highlighting the importance of precise dental segmentation. This study leverages active learning with the three-dimensional (3D) nnU-net and multi-labels to improve segmentation accuracy of dental anatomies, includi...
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Nature Portfolio
2025-03-01
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| Online Access: | https://doi.org/10.1038/s41598-025-91725-2 |
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| author | Sungchul On Junhyeok Ock Myungsoo Bae Jae-Woo Park Seung-Hak Baek Sungwon Ham Namkug Kim |
| author_facet | Sungchul On Junhyeok Ock Myungsoo Bae Jae-Woo Park Seung-Hak Baek Sungwon Ham Namkug Kim |
| author_sort | Sungchul On |
| collection | DOAJ |
| description | Abstract Recent advancements in deep learning have revolutionized digital dentistry, highlighting the importance of precise dental segmentation. This study leverages active learning with the three-dimensional (3D) nnU-net and multi-labels to improve segmentation accuracy of dental anatomies, including the maxillary sinuses, maxilla, mandible, and inferior alveolar nerves (IAN), which are important for implant planning, in 3D cone-beam computed tomography (CBCT) scans. Segmentation accuracy was compared using single-label, adjacent pair-label, and multi-label relevant anatomic structures with 60 CBCT scans from Kooalldam Dental Hospital and externally validated using data from Seoul National University Dental Hospital. The dataset was divided into three training stages for active learning. The evaluation metrics were assessed through the Dice similarity coefficient (DSC) and mean absolute difference. The overall internal test set DSCs from the multi-label, single-label, and pair-label models were 95%, 91% (paired t-test; p = 0.01), and 93% (p = 0.03), respectively. The DSC of the IAN in the internal and external datasets increased from 83% to 79%, 87% and 81%, to 90% and 86% for the single-label, pair-label, and multi-label models, respectively (all p = 0.01). Prediction accuracy improved over time, significantly reducing the manual correction time. Our active learning and multi-label strategies facilitated accurate automatic segmentation. |
| format | Article |
| id | doaj-art-150bd5ec739b43cfbe6aea2e2cab8aa6 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-150bd5ec739b43cfbe6aea2e2cab8aa62025-08-20T02:59:20ZengNature PortfolioScientific Reports2045-23222025-03-0115111010.1038/s41598-025-91725-2Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomographySungchul On0Junhyeok Ock1Myungsoo Bae2Jae-Woo Park3Seung-Hak Baek4Sungwon Ham5Namkug Kim6Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Orthodontics, School of Dentistry, Seoul National UniversityHealthcare Readiness Institute for Unified Korea, Korea Univerisity Ansan Hospital, Korea University College of MedicineDepartment of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineAbstract Recent advancements in deep learning have revolutionized digital dentistry, highlighting the importance of precise dental segmentation. This study leverages active learning with the three-dimensional (3D) nnU-net and multi-labels to improve segmentation accuracy of dental anatomies, including the maxillary sinuses, maxilla, mandible, and inferior alveolar nerves (IAN), which are important for implant planning, in 3D cone-beam computed tomography (CBCT) scans. Segmentation accuracy was compared using single-label, adjacent pair-label, and multi-label relevant anatomic structures with 60 CBCT scans from Kooalldam Dental Hospital and externally validated using data from Seoul National University Dental Hospital. The dataset was divided into three training stages for active learning. The evaluation metrics were assessed through the Dice similarity coefficient (DSC) and mean absolute difference. The overall internal test set DSCs from the multi-label, single-label, and pair-label models were 95%, 91% (paired t-test; p = 0.01), and 93% (p = 0.03), respectively. The DSC of the IAN in the internal and external datasets increased from 83% to 79%, 87% and 81%, to 90% and 86% for the single-label, pair-label, and multi-label models, respectively (all p = 0.01). Prediction accuracy improved over time, significantly reducing the manual correction time. Our active learning and multi-label strategies facilitated accurate automatic segmentation.https://doi.org/10.1038/s41598-025-91725-2Active learningCone-beam computed tomographyDental segmentationInferior alveolar nerveMulti-label segmentation |
| spellingShingle | Sungchul On Junhyeok Ock Myungsoo Bae Jae-Woo Park Seung-Hak Baek Sungwon Ham Namkug Kim Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography Scientific Reports Active learning Cone-beam computed tomography Dental segmentation Inferior alveolar nerve Multi-label segmentation |
| title | Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography |
| title_full | Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography |
| title_fullStr | Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography |
| title_full_unstemmed | Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography |
| title_short | Improving accuracy for inferior alveolar nerve segmentation with multi-label of anatomical adjacent structures using active learning in cone-beam computed tomography |
| title_sort | improving accuracy for inferior alveolar nerve segmentation with multi label of anatomical adjacent structures using active learning in cone beam computed tomography |
| topic | Active learning Cone-beam computed tomography Dental segmentation Inferior alveolar nerve Multi-label segmentation |
| url | https://doi.org/10.1038/s41598-025-91725-2 |
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