Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images

Abstract

Accurate segmentation of oral surgery-related tissues from cone beam computed tomography (CBCT) images can significantly accelerate treatment planning and improve surgical accuracy. In this paper, we propose a fully automated tissue segmentation system for dental implant surgery. Specifically, we propose an image preprocessing method based on data distribution histograms, which can adaptively process CBCT images with different parameters. Based on this, we use the bone segmentation network to obtain the segmentation results of alveolar bone, teeth, and maxillary sinus. We use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding tasks. The tooth segmentation results can obtain the order information of the dentition. The corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing methods. Its average Dice scores on the tooth, alveolar bone, maxillary sinus, and mandibular canal segmentation tasks were 96.5%, 95.4%, 93.6%, and 94.8%, respectively. These results demonstrate that it can accelerate the development of digital dentistry.

References

1. 1.Bai, S. Z.et al. [Animal experiment on the accuracy of the Autonomous Dental Implant Robotic System].Zhonghua Kou Qiang Yi Xue Za Zhi 56, 170-174 (2021).
2. Jia S., Wang G., Zhao Y.& Wang, X. Accuracy of an autonomous dental implant robotic system versus static guide-assisted implant surgery: a retrospective clinical study. J. Prosthet. Dent. https://doi.org/10.1016/j.prosdent.2023.04.027(2023).
3. Li Z., Xie R., Bai S.& Zhao, Y. Implant placement with an autonomous dental implant robot: a clinical report. J. Prosthet. Dent. https://doi.org/10.1016/j.prosdent.2023.02.014(2023).
4. Wu Q., Zhao Y. M., Bai S. Z.& Li, X. Application of robotics in stomatology.Int. J. Comput. Dent. 22, 251-260 (2019).
5. Cheng, K. J.et al.Accuracy of dental implant surgery with robotic position feedback and registration algorithm: an in-vitro study.Comput. Biol. Med. 129, 104153(2021).
6. Tao, B.et al.Accuracy of dental implant surgery using dynamic navigation and robotic systems: an in vitro study.J. Dent. 123, 104170(2022).
7. Bolding S. L.& Reebye, U. N. Accuracy of haptic robotic guidance of dental implant surgery for completely edentulous arches.J. Prosthet. Dent. 128, 639-647 (2022).
8. Yang, X.et al.ImplantFormer: vision transformer based implant position regression using dental CBCT data. Accessed July 3, 2023. Preprint at ImplantFormer: vision transformer based implant position regression using dental CBCT data. Accessed July 3, 2023. Preprint at http://arxiv.org/abs/2210.16467 (2023.
9. Płotka, S.et al.Convolutional neural networks in orthodontics: a review. Accessed July 3, 2023. Preprint at Convolutional neural networks in orthodontics: a review. Accessed July 3, 2023. Preprint at http://arxiv.org/abs/2104.08886 (2021.
10. Duy, N. T., Lamecker, H., Kainmueller, D. & Zachow, S. Automatic detection and classification of teeth in CT data. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, Lecture Notes in Computer Science (eds Ayache, N., Delingette, H., Golland, P. & Mori, K.) 609-616 (Springer, 2012).
11. Barone, S., Paoli, A.& Razionale, A. V. CT segmentation of dental shapes by anatomy-driven reformation imaging and B-spline modelling.Int. J. Numer. Method. Biomed. Eng. 32, e02747(2016).
12. Sepehrian, M., Deylami, A. M. & Zoroofi, R. A. Individual teeth segmentation in CBCT and MSCT dental images using watershed. In 2013 20th Iranian Conference on Biomedical Engineering (ICBME) 27-30 (IEEE, 2013).
13. Gao H.& Chae, O. Individual tooth segmentation from CT images using level set method with shape and intensity prior.Pattern Recognit. 43, 2406-2417 (2010).
14. Qian, J.et al.An automatic tooth reconstruction method based on multimodal data.J. Vis. 24, 205-221 (2021).
15. Poonsri, A., Aimjirakul, N., Charoenpong, T. & Sukjamsri, C. Teeth segmentation from dental X-ray image by template matching. In 2016 9th Biomedical Engineering International Conference (BMEiCON) 1-4 (IEEE, 2016).
16. Akhoondali, H., Zoroofi, R. A.& Shirani, G. Rapid automatic segmentation and visualization of teeth in CT-scan data.J. Appl. Sci. 9, 2031-2044 (2009).
17. Zou X., Liu W., Wang J., Tao R.& Zheng, G. ARST: auto-regressive surgical transformer for phase recognition from laparoscopic videos. Accessed July 3, 2023. Preprint at & Zheng, G. ARST: auto-regressive surgical transformer for phase recognition from laparoscopic videos. Accessed July 3, 2023. Preprint at http://arxiv.org/abs/2209.01148 (2022.
18. Huang S., Xu T., Shen N., Mu F.& Li, J.Rethinking few-shot medical segmentation: a vector quantization view.
19. Zhang J., Xie Y., Xia Y.& Shen, C. DoDNet: learning to segment multi-organ and tumors from multiple partially labeled datasets. Accessed July 3, 2023. Preprint at & Shen, C. DoDNet: learning to segment multi-organ and tumors from multiple partially labeled datasets. Accessed July 3, 2023. Preprint at http://arxiv.org/abs/2011.10217 (2020.
20. Wang, R.et al.Medical image segmentation using deep learning: a survey.IET Image Process. 16, 1243-1267 (2022).
21. Zhang, R.et al.Multiple supervised residual network for osteosarcoma segmentation in CT images.Comput. Med. Imaging Graph. 63, 1-8 (2018).
22. Tao, R., Liu, W.& Zheng, G. Spine-transformers: vertebra labeling and segmentation in arbitrary field-of-view spine CTs via 3D transformers.Med. Image Anal. 75, 102258(2022).
23. Cai, Y.et al.Swin Unet3D: a three-dimensional medical image segmentation network combining vision transformer and convolution.BMC Med. Inf. Decis. Mak. 23, 33(2023).
24. Cao, H. et al. Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation. In Computer Vision - ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, Vol. 13803 (eds. Karlinsky, L., Michaeli, T. & Nishino, K.) https://doi.org/10.1007/978-3-031-25066-8_9 (Springer, Cham, 2023).
25. Hatamizadeh, A.et al.Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. https://arxiv.org/abs/2201.01266(2022).
26. 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, CA, USA. 565-571, https://doi.org/10.1109/3DV.2016.79(2016).
27. Cui, Z.et al.A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.Nat. Commun. 13, 2096(2022).
28. Chen, Y.et al.Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task FCN.IEEE Access 8, 97296-97309 (2020).
29. Jaskari, J.et al.Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes.Sci. Rep. 10, 5842(2020).
30. Verhelst, P. J.et al.Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography.J. Dent. 114, 103786(2021).
31. Chung, M.et al.Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation.Comput. Biol. Med. 120, 103720(2020).
32. Cui, Z., Li, C. & Wang, W. ToothNet: automatic tooth instance segmentation and identification from cone beam CT Images. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 6361-6370 (IEEE, 2019).
33. Lee, J.et al.Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement.Multimed Tools Appl. 81, 18327-18342 (2022).
34. Gerhardt M. D.N. et al. Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using convolutional neural networks.J. Dent. 122, 104139(2022).
35. Liu, J.et al.Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction.Patterns 4, 100825(2023).
36. Nackaerts, O.et al.Segmentation of trabecular jaw bone on cone beam CT datasets: segmentation of jaw bone on CBCT datasets.Clin. Implant Dent. Relat. Res. 17, 1082-1091 (2015).
37. FDI World Dental Federation. Accessed July 11, 2023. https://web.archive.org/web/20070401074213/. Accessed July 11, 2023. https://web.archive.org/web/20070401074213/http://www.fdiworldental.org/resources/5_0notation.html (2007.
38. Cui, Z., et al.Hierarchical morphology-guided tooth instance segmentation from CBCT images. In Information Processing in Medical Imaging, Lecture Notes in Computer Science (eds Feragen, A., Sommer, S., Schnabel, J. & Nielsen, M.) 150-162
(Springer International Publishing, 2021)..
39. Isensee F., Jäger P. F., Kohl S. A.A., Petersen, J. & Maier-Hein, K. H. Automated design of deep learning methods for biomedical image segmentation.Nat. Methods 18, 203-211 (2021).
40. Wu, X. et al. Center-sensitive and boundary-aware tooth instance segmentation and classification from cone-beam CT. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) 939-942 (IEEE, 2020).
41. Zhang Y.& Yu, H. Convolutional neural network based metal artifact reduction in X-ray computed tomography.IEEE Trans. Med. Imaging 37, 1370-1381 (2018).
42. Liang, X.et al.Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy.Phys. Med. Biol. 64, 125002(2019).
43. Liu, Y.et al.CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy.Med. Phys. 47, 2472-2483 (2020).
44. Galibourg, A.et al.Assessment of automatic segmentation of teeth using a watershed-based method.Dentomaxillofac. Radiol. 47, 20170220(2018).
45. Hounsfield G. N.Computed medical imaging.Science 210, 22-28 (1980).
46. Özgün, Ç., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. International Conference on Medical Image Computing and Computer-Assisted Intervention (2016).
47. Vaswani, A.et al. Attention is All you Need. Adv. Neural Inf. Process. Syst. (2017).
48. Dosovitskiy, A.et al.An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. https://arxiv.org/abs/2010.11929(2020).
49. Han, K.et al.A survey on Vision Transformer.IEEE Trans. Pattern Anal. Mach. Intell. 45, 87-110 (2023).
50. Liu, Z. et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) 9992-10002 (2021).
51. 51.Hatamizadeh, A., Yang, D., Roth, H. R. & Xu, D. UNETR: Transformers for 3D Medical Image Segmentation. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 1748-1758 (2021).