Study on dental image segmentation and automatic root canal measurement based on multi-stage deep learning using cone beam computed tomography_Journal of Biomedical Engineering

Authors：

CHEN Ziqing ¹ ,  LIU Qi ¹ , WANG Jialei ² , JI Nuo ¹ , GONG Yuhang ¹ , GAO Bo ²

1. School of Biomedical Engineering, Sichuan University, Chengdu 610065, P. R. China;
2. National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

LIU Qi, Email: liuqi@scu.edu.cn

Keywords：

Tooth instance segmentation; Cone beam computed tomography; Root canal measurement; Deep learning

DOI：

10.7507/1001-5515.202503008

Video：

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This study aims to develop a fully automated method for tooth segmentation and root canal measurement based on cone beam computed tomography (CBCT) images, providing objective, efficient, and accurate measurement results to guide and assist clinicians in root canal diagnosis grading, instrument selection, and preoperative planning. The method utilized Attention U-Net to recognize tooth descriptors, cropped regions of interest (ROIs) based on the center of mass of these descriptors, and applied an integrated deep learning method for segmentation. The segmentation results were mapped back to the original coordinates and position-corrected, followed by automatic measurement and visualization of root canal lengths and angles. The results indicated that the Dice coefficient for segmentation was 96.42%, the Jaccard coefficient was 93.11%, the Hausdorff Distance was 2.07 mm, and the average surface distance was 0.23 mm, all surpassed existing methods. The relative error of the root canal working length measurement was 3.15% (< 5%), the curvature angle error was 2.85 °, and the correct classification rate of the treatment difficulty coefficient was 90.48%. The proposed methods all achieved favorable results, which can provide an important reference for clinical application.

1.	Cui Z, Fang Y, Mei L, et al. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nat Commun, 2022, 13(1): 2096.
2.	Liang Y H, Jiang L, Chen C, et al. The validity of cone-beam computed tomography in measuring root canal length using a gold standard. J Endod, 2013, 39(12): 1607-1610.
3.	Segato A V K, Piasecki L, Nuñovero M F I, et al. The accuracy of a new cone-beam computed tomographic software in the preoperative working length determination ex vivo. J Endod, 2018, 44(6): 1024-1029.
4.	Janner S F M, Jeger F B, Lussi A, et al. Precision of endodontic working length measurements: a pilot investigation comparing cone-beam computed tomography scanning with standard measurement techniques. J Endod, 2011, 37(8): 1046-1051.
5.	Li Z, Ning X, Wang Z. A fast segmentation method for STL teeth model// ICME International Conference on Complex Medical Engineering. Beijing: IEEE, 2007: 163-166.
6.	Wongwaen N, Sinthanayothin C. Computerized algorithm for 3D teeth segmentation// 2010 International Conference on Electronics and Information Engineering. Kyoto: IEEE, 2010, 1: V1-277-V1-280.
7.	Hosntalab M, Aghaeizadeh Zoroofi R, Abbaspour Tehrani-Fard A, et al. Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set. Int J Comput Ass Rad, 2008, 3: 257-265.
8.	Zhang J, Xia W, Dong J, et al. Root canal segmentation in CBCT images by 3D U-net with global and local combination loss// 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Electr Network: IEEE, 2021: 3097-3100.
9.	Al-Sherif N, Guo G, Ammar H H. A new approach to teeth segmentation// 2012 IEEE International Symposium on Multimedia. Irvine: IEEE, 2012: 145-148.
10.	Hiew L T, Ong S H, Foong K W C, et al. Tooth segmentation from cone-beam CT using graph cut// Proceedings of the Second APSIPA Annual Summit and Conference. Singapore: ASC, 2010: 272-275.
11.	Keustermans J, Vandermeulen D, Suetens P. Integrating statistical shape models into a graph cut framework for tooth segmentation// Wang F, Shen D, Yan P, et al. Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2012, 7588: 242-249.
12.	Evain T, Ripoche X, Atif J, et al. Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape priors// 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne: IEEE, 2017: 1197-1200.
13.	Keyhaninejad S, Zoroofi R A, Setarehdan S K, et al. Automated segmentation of teeth in multi-slice CT images// IET International Conference on Visual Information Engineering (VIE 2006). Stevenage: IET, 2006: 339-344.
14.	Gao H, Chae O. Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recogn, 2010, 43(7): 2406-2417.
15.	Ji D X, Ong S H, Foong K W C. A level-set based approach for anterior teeth segmentation in cone beam computed tomography images. Comput Biol Med, 2014, 50: 116-128.
16.	Gan Y, Xia Z, Xiong J, et al. Tooth and alveolar bone segmentation from dental computed tomography images. IEEE J Biomed Health Inform, 2017, 22(1): 196-204.
17.	Gan Y, Xia Z, Xiong J, et al. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Med Phys, 2015, 42(1): 14-27.
18.	Wang Y, Liu S, Wang G, et al. Accurate tooth segmentation with improved hybrid active contour model. Phys Med Biol, 2018, 64(1): 015012.
19.	Polizzi A, Quinzi V, Ronsivalle V, et al. Tooth automatic segmentation from CBCT images: a systematic review. Clin Oral Investig, 2023, 27(7): 3363-3378.
20.	Tarce M, Zhou Y, Antonelli A, et al. The application of artificial intelligence for tooth segmentation in CBCT images: A systematic review. Appl Sci, 2024, 14(14): 6298.
21.	Santos‐Junior A O, Fontenele R C, Neves F S, et al. A novel artificial intelligence‐powered tool for automated root canal segmentation in single‐rooted teeth on cone‐beam computed tomography. Int Endod J, 2025, 58(4): 658-671.
22.	Cui Z, Li C, Wang W. ToothNet: automatic tooth instance segmentation and identification from cone beam CT images// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 6368-6377.
23.	Chung M, Lee M, Hong J, et al. Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. Comput Biol Med, 2020, 120: 103720.
24.	Chen Y, Du H, Yun Z, et al. Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task FCN. IEEE Access, 2020, 8: 97296-97309.
25.	Dot G, Chaurasia A, Dubois G, et al. DentalSegmentator: robust open source deep learning-based CT and CBCT image segmentation. J Dent, 2024, 147: 105130.
26.	Shaheen E, Leite A, Alqahtani K A, et al. A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study. J Dent, 2021, 115: 103865.
27.	Wang Y, Xia W, Yan Z, et al. Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning. Med Image Anal, 2023, 85: 102750.
28.	Gong Y, Zhang J, Cheng J, et al. Automatic tooth segmentation for patients with alveolar clefts guided by tooth descriptors. Biomed Signal Process Control, 2024, 90: 105821.
29.	Tan M, Cui Z, Zhong T, et al. A progressive framework for tooth and substructure segmentation from cone-beam CT images. Comput Biol Med, 2024, 169: 107839.
30.	Wu X, Chen H, Huang Y, et al. Center-sensitive and boundary-aware tooth instance segmentation and classification from cone-beam CT// 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). Iowa: IEEE, 2020: 939-942.
31.	Cui Z, Zhang B, Lian C, et al. Hierarchical morphology-guided tooth instance segmentation from CBCT images// Feragen A, Sommer S, Schnabel J, et al. Information Processing in Medical Imaging. IPMI 2021. Lecture Notes in Computer Science. Cham: Springer, 2021, 12729: 150-162.
32.	Schäfer E, Diez C, Hoppe W, et al. Roentgenographic investigation of frequency and degree of canal curvatures in human permanent teeth. J Endod, 2002, 28(3): 211-216.
33.	Park P S, Kim K D, Perinpanayagam H, et al. Three-dimensional analysis of root canal curvature and direction of maxillary lateral incisors by using cone-beam computed tomography. J Endod, 2013, 39(9): 1124-1129.
34.	Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: Learning where to look for the pancreas. arxiv preprint arxiv, 2018: 1804.03999.
35.	Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation// 2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571.
36.	Sharma M C, Arora V. Determination of working length of root canal. Med J Armed Forces India, 2010, 66(3): 231-234.
37.	黄定明. 根管治疗难度系数临床评估标准. 华西口腔医学杂志, 2004, 22(5): 381-383.

1. Cui Z, Fang Y, Mei L, et al. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nat Commun, 2022, 13(1): 2096.
2. Liang Y H, Jiang L, Chen C, et al. The validity of cone-beam computed tomography in measuring root canal length using a gold standard. J Endod, 2013, 39(12): 1607-1610.
3. Segato A V K, Piasecki L, Nuñovero M F I, et al. The accuracy of a new cone-beam computed tomographic software in the preoperative working length determination ex vivo. J Endod, 2018, 44(6): 1024-1029.
4. Janner S F M, Jeger F B, Lussi A, et al. Precision of endodontic working length measurements: a pilot investigation comparing cone-beam computed tomography scanning with standard measurement techniques. J Endod, 2011, 37(8): 1046-1051.
5. Li Z, Ning X, Wang Z. A fast segmentation method for STL teeth model// ICME International Conference on Complex Medical Engineering. Beijing: IEEE, 2007: 163-166.
6. Wongwaen N, Sinthanayothin C. Computerized algorithm for 3D teeth segmentation// 2010 International Conference on Electronics and Information Engineering. Kyoto: IEEE, 2010, 1: V1-277-V1-280.
7. Hosntalab M, Aghaeizadeh Zoroofi R, Abbaspour Tehrani-Fard A, et al. Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set. Int J Comput Ass Rad, 2008, 3: 257-265.
8. Zhang J, Xia W, Dong J, et al. Root canal segmentation in CBCT images by 3D U-net with global and local combination loss// 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Electr Network: IEEE, 2021: 3097-3100.
9. Al-Sherif N, Guo G, Ammar H H. A new approach to teeth segmentation// 2012 IEEE International Symposium on Multimedia. Irvine: IEEE, 2012: 145-148.
10. Hiew L T, Ong S H, Foong K W C, et al. Tooth segmentation from cone-beam CT using graph cut// Proceedings of the Second APSIPA Annual Summit and Conference. Singapore: ASC, 2010: 272-275.
11. Keustermans J, Vandermeulen D, Suetens P. Integrating statistical shape models into a graph cut framework for tooth segmentation// Wang F, Shen D, Yan P, et al. Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2012, 7588: 242-249.
12. Evain T, Ripoche X, Atif J, et al. Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape priors// 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne: IEEE, 2017: 1197-1200.
13. Keyhaninejad S, Zoroofi R A, Setarehdan S K, et al. Automated segmentation of teeth in multi-slice CT images// IET International Conference on Visual Information Engineering (VIE 2006). Stevenage: IET, 2006: 339-344.
14. Gao H, Chae O. Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recogn, 2010, 43(7): 2406-2417.
15. Ji D X, Ong S H, Foong K W C. A level-set based approach for anterior teeth segmentation in cone beam computed tomography images. Comput Biol Med, 2014, 50: 116-128.
16. Gan Y, Xia Z, Xiong J, et al. Tooth and alveolar bone segmentation from dental computed tomography images. IEEE J Biomed Health Inform, 2017, 22(1): 196-204.
17. Gan Y, Xia Z, Xiong J, et al. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Med Phys, 2015, 42(1): 14-27.
18. Wang Y, Liu S, Wang G, et al. Accurate tooth segmentation with improved hybrid active contour model. Phys Med Biol, 2018, 64(1): 015012.
19. Polizzi A, Quinzi V, Ronsivalle V, et al. Tooth automatic segmentation from CBCT images: a systematic review. Clin Oral Investig, 2023, 27(7): 3363-3378.
20. Tarce M, Zhou Y, Antonelli A, et al. The application of artificial intelligence for tooth segmentation in CBCT images: A systematic review. Appl Sci, 2024, 14(14): 6298.
21. Santos‐Junior A O, Fontenele R C, Neves F S, et al. A novel artificial intelligence‐powered tool for automated root canal segmentation in single‐rooted teeth on cone‐beam computed tomography. Int Endod J, 2025, 58(4): 658-671.
22. Cui Z, Li C, Wang W. ToothNet: automatic tooth instance segmentation and identification from cone beam CT images// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 6368-6377.
23. Chung M, Lee M, Hong J, et al. Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. Comput Biol Med, 2020, 120: 103720.
24. Chen Y, Du H, Yun Z, et al. Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task FCN. IEEE Access, 2020, 8: 97296-97309.
25. Dot G, Chaurasia A, Dubois G, et al. DentalSegmentator: robust open source deep learning-based CT and CBCT image segmentation. J Dent, 2024, 147: 105130.
26. Shaheen E, Leite A, Alqahtani K A, et al. A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study. J Dent, 2021, 115: 103865.
27. Wang Y, Xia W, Yan Z, et al. Root canal treatment planning by automatic tooth and root canal segmentation in dental CBCT with deep multi-task feature learning. Med Image Anal, 2023, 85: 102750.
28. Gong Y, Zhang J, Cheng J, et al. Automatic tooth segmentation for patients with alveolar clefts guided by tooth descriptors. Biomed Signal Process Control, 2024, 90: 105821.
29. Tan M, Cui Z, Zhong T, et al. A progressive framework for tooth and substructure segmentation from cone-beam CT images. Comput Biol Med, 2024, 169: 107839.
30. Wu X, Chen H, Huang Y, et al. Center-sensitive and boundary-aware tooth instance segmentation and classification from cone-beam CT// 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). Iowa: IEEE, 2020: 939-942.
31. Cui Z, Zhang B, Lian C, et al. Hierarchical morphology-guided tooth instance segmentation from CBCT images// Feragen A, Sommer S, Schnabel J, et al. Information Processing in Medical Imaging. IPMI 2021. Lecture Notes in Computer Science. Cham: Springer, 2021, 12729: 150-162.
32. Schäfer E, Diez C, Hoppe W, et al. Roentgenographic investigation of frequency and degree of canal curvatures in human permanent teeth. J Endod, 2002, 28(3): 211-216.
33. Park P S, Kim K D, Perinpanayagam H, et al. Three-dimensional analysis of root canal curvature and direction of maxillary lateral incisors by using cone-beam computed tomography. J Endod, 2013, 39(9): 1124-1129.
34. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: Learning where to look for the pancreas. arxiv preprint arxiv, 2018: 1804.03999.
35. Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation// 2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571.
36. Sharma M C, Arora V. Determination of working length of root canal. Med J Armed Forces India, 2010, 66(3): 231-234.
37. 黄定明. 根管治疗难度系数临床评估标准. 华西口腔医学杂志, 2004, 22(5): 381-383.

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