Automatic mandibular canal detection using a deep convolutional neural network.
Adolescent
Adult
Aged
Aged, 80 and over
Cone-Beam Computed Tomography
/ methods
Deep Learning
Female
Humans
Imaging, Three-Dimensional
/ methods
Male
Mandible
/ diagnostic imaging
Mandibular Nerve
/ diagnostic imaging
Middle Aged
Neural Networks, Computer
Patient Care Planning
Temporomandibular Joint Disorders
/ diagnostic imaging
Young Adult
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 03 2020
31 03 2020
Historique:
received:
13
05
2019
accepted:
16
03
2020
entrez:
3
4
2020
pubmed:
3
4
2020
medline:
2
12
2020
Statut:
epublish
Résumé
The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients' discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.
Identifiants
pubmed: 32235882
doi: 10.1038/s41598-020-62586-8
pii: 10.1038/s41598-020-62586-8
pmc: PMC7109125
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5711Références
Ghatak, R. N. & Anatomy, G. J. Head and Neck, Mandibular Nerve. (2018).
Phillips, C. & Essick, G. Inferior alveolar nerve injury following orthognathic surgery: a review of assessment issues. Journal of oral rehabilitation 38, 547–554, https://doi.org/10.1111/j.1365-2842.2010.02176.x (2011).
doi: 10.1111/j.1365-2842.2010.02176.x
pubmed: 21058973
Sarikov, R. & Juodzbalys, G. Inferior alveolar nerve injury after mandibular third molar extraction: a literature review. Journal of oral & maxillofacial research 5, e1–e1, https://doi.org/10.5037/jomr.2014.5401 (2014).
doi: 10.5037/jomr.2014.5401
Shavit, I. & Juodzbalys, G. Inferior alveolar nerve injuries following implant placement - importance of early diagnosis and treatment: a systematic review. Journal of oral & maxillofacial research 5, e2–e2, https://doi.org/10.5037/jomr.2014.5402 (2014).
doi: 10.5037/jomr.2014.5402
Ai, C. J., Jabar, N. A., Lan, T. H. & Ramli, R. Mandibular Canal Enlargement: Clinical and Radiological Characteristics. Journal of clinical imaging science 7, 28–28, https://doi.org/10.4103/jcis.JCIS_28_17 (2017).
doi: 10.4103/jcis.JCIS_28_17
pubmed: 28781925
pmcid: 5523564
Jung, Y.-H. & Cho, B.-H. Radiographic evaluation of the course and visibility of the mandibular canal. Imaging science in dentistry 44, 273–278, https://doi.org/10.5624/isd.2014.44.4.273 (2014).
doi: 10.5624/isd.2014.44.4.273
pubmed: 25473634
pmcid: 4245468
Jaju, P. P. & Jaju, S. P. Clinical utility of dental cone-beam computed tomography: current perspectives. Clinical, cosmetic and investigational dentistry 6, 29–43, https://doi.org/10.2147/CCIDE.S41621 (2014).
doi: 10.2147/CCIDE.S41621
pubmed: 24729729
pmcid: 3979889
Scarfe, W. C. & Farman, A. G. What is Cone-Beam CT and How Does it Work? Dental Clinics of North America 52, 707–730, https://doi.org/10.1016/j.cden.2008.05.005 (2008).
doi: 10.1016/j.cden.2008.05.005
pubmed: 18805225
Al-Okshi, A., Lindh, C., Salé, H., Gunnarsson, M. & Rohlin, M. Effective dose of cone beam CT (CBCT) of the facial skeleton: a systematic review. The British journal of radiology 88, 20140658–20140658, https://doi.org/10.1259/bjr.20140658 (2015).
doi: 10.1259/bjr.20140658
pubmed: 25486387
Pauwels, R., Jacobs, R., Singer, S. R. & Mupparapu, M. CBCT-based bone quality assessment: are Hounsfield units applicable? Dento maxillo facial radiology 44, 20140238–20140238, https://doi.org/10.1259/dmfr.20140238 (2015).
doi: 10.1259/dmfr.20140238
pubmed: 25315442
Hwang, J.-J., Jung, Y.-H., Cho, B.-H. & Heo, M.-S. An overview of deep learning in the field of dentistry. Imaging science in dentistry 49, 1–7, https://doi.org/10.5624/isd.2019.49.1.1 (2019).
doi: 10.5624/isd.2019.49.1.1
pubmed: 30941282
pmcid: 6444007
Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 3320–3328 (MIT Press, Montreal, Canada (2014).
Nishio, M. et al. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PloS one 13, e0200721, https://doi.org/10.1371/journal.pone.0200721 (2018).
doi: 10.1371/journal.pone.0200721
pubmed: 30052644
pmcid: 6063408
Hyun-Jung Kwak, G. & Hui, P. J. a. p. a. DeepHealth: Deep Learning for Health Informatics. (2019).
Shan, H. et al. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network. IEEE Transactions on Medical Imaging 37, 1522–1534, https://doi.org/10.1109/TMI.2018.2832217 (2018).
doi: 10.1109/TMI.2018.2832217
pubmed: 29870379
pmcid: 6022756
Vinayahalingam, S., Xi, T., Berge, S., Maal, T. & De Jong, G. Automated detection of third molars and mandibular nerve by deep learning. Scientific Reports 9, https://doi.org/10.1038/s41598-019-45487-3 (2019).
Ronneberger, O., Fischer, P. & Brox, T. In International Conference on Medical image computing and computer-assisted intervention. 234–241 (Springer).
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9901, 424–432, https://doi.org/10.1007/978-3-319-46723-8_49 (2016).
doi: 10.1007/978-3-319-46723-8_49
Badrinarayanan, V., Kendall, A. & Cipolla, R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE transactions on pattern analysis and machine intelligence 39, 2481–2495, https://doi.org/10.1109/tpami.2016.2644615 (2017).
doi: 10.1109/tpami.2016.2644615
pubmed: 28060704
Simonyan, K. & Zisserman, A. J. a. p. a. Very deep convolutional networks for large-scale image recognition. (2014).
Long, J., Shelhamer, E. & Darrell, T. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431–3440.
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Very Deep Convolutional Networks for Large-scale Image Recognition (2014).
Eigen, D., Fergus, R., Eigen, D. & Fergus, R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In Proceedings of the IEEE international conference on computer vision. 2650–2658 (2015).
Kingma, D. & Ba, J. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (2014).
Moris, B., Claesen, L., Yi, S. & Politis, C. Fourth International Conference on Communications and Electronics (ICCE). 327–332. (2012).
Kim, S. T. et al. Location of the mandibular canal and the topography of its neurovascular structures. The Journal of craniofacial surgery 20, 936–939, https://doi.org/10.1097/SCS.0b013e3181a14c79 (2009).
doi: 10.1097/SCS.0b013e3181a14c79
pubmed: 19461335
Lee, H. E. & Han, S. J. Anatomical position of the mandibular canal in relation to the buccal cortical bone: relevance to sagittal split osteotomy. Journal of the Korean Association of Oral and Maxillofacial Surgeons 44, 167–173, https://doi.org/10.5125/jkaoms.2018.44.4.167 (2018).
doi: 10.5125/jkaoms.2018.44.4.167
pubmed: 30181983
pmcid: 6117468
Oliveira-Santos, C. et al. Visibility of the mandibular canal on CBCT cross-sectional images. Journal of applied oral science: revista FOB 19, 240–243, https://doi.org/10.1590/S1678-77572011000300011 (2011).
doi: 10.1590/S1678-77572011000300011
pubmed: 21625740
Gu, L., Zhu, C., Chen, K., Liu, X. & Tang, Z. Anatomic study of the position of the mandibular canal and corresponding mandibular third molar on cone-beam computed tomography images. Surgical and radiologic anatomy: SRA 40, 609–614, https://doi.org/10.1007/s00276-017-1928-6 (2018).
doi: 10.1007/s00276-017-1928-6
pubmed: 29079941
Kroon, D.-J. Segmentation of the mandibular canal in cone-beam CT data. (2011).
Abdolali, F. et al. Automatic segmentation of mandibular canal in cone beam CT images using conditional statistical shape model and fast marching. International journal of computer assisted radiology and surgery 12, 581–593, https://doi.org/10.1007/s11548-016-1484-2 (2017).
doi: 10.1007/s11548-016-1484-2
pubmed: 27653614
Gerlach, N. L. et al. Evaluation of the potential of automatic segmentation of the mandibular canal using cone-beam computed tomography. The British journal of oral & maxillofacial surgery 52, 838–844, https://doi.org/10.1016/j.bjoms.2014.07.253 (2014).
doi: 10.1016/j.bjoms.2014.07.253
Razzak, M. I., Naz, S. & Zaib, A. In Classification in BioApps 323–350 (Springer (2018).
Roy, S., Krishna, G., Dubey, S. R. & Chaudhuri, B. HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification. (2019).