{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:17:28Z","timestamp":1777004248863,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts manually determine the implant position and dimensions from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal. In particular, we first enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme, which improves the visibility of mandibular canals. After enhancement, we designed 3D deeply supervised attention UNet architecture for localizing the Volumes Of Interest (VOIs), which contain the mandibular canals (i.e., left and right canals). Finally, we employed the Multi-Scale input Residual UNet (MSiR-UNet) architecture to segment the mandibular canals using VOIs accurately. The proposed method has been rigorously evaluated on 500 and 15 CBCT scans from our dataset and from the public dataset, respectively. The results demonstrate that our technique improves the existing performance of mandibular canal segmentation to a clinically acceptable range. Moreover, it is robust against the types of CBCT scans in terms of field of view.<\/jats:p>","DOI":"10.3390\/s22249877","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T03:55:39Z","timestamp":1671162939000},"page":"9877","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7278-8488","authenticated-orcid":false,"given":"Muhammad","family":"Usman","sequence":"first","affiliation":[{"name":"Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co., Ltd., Seoul 06524, Republic of Korea"},{"name":"Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7085-0105","authenticated-orcid":false,"given":"Azka","family":"Rehman","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co., Ltd., Seoul 06524, Republic of Korea"}]},{"given":"Amal Muhammad","family":"Saleem","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co., Ltd., Seoul 06524, Republic of Korea"}]},{"given":"Rabeea","family":"Jawaid","sequence":"additional","affiliation":[{"name":"Division of AI and Computer Engineering, Kyonggi University, Suwon 16227, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7189-4000","authenticated-orcid":false,"given":"Shi-Sub","family":"Byon","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co., Ltd., Seoul 06524, Republic of Korea"}]},{"given":"Sung-Hyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co., Ltd., Seoul 06524, Republic of Korea"}]},{"given":"Byoung-Dai","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of AI and Computer Engineering, Kyonggi University, Suwon 16227, Republic of Korea"}]},{"given":"Min-Suk","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea"}]},{"given":"Yeong-Gil","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/S0887-2171(02)90047-8","article-title":"The facial nerve: Anatomy and common pathology","volume":"Volume 23","author":"Phillips","year":"2002","journal-title":"Proceedings of the Seminars in Ultrasound, CT and MRI"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e431","DOI":"10.1016\/j.joms.2011.07.013","article-title":"The impact of altered sensation affecting the lower lip after orthognathic treatment","volume":"69","author":"Lee","year":"2011","journal-title":"J. Oral Maxillofac. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.5037\/jomr.2011.2101","article-title":"Injury of the inferior alveolar nerve during implant placement: A literature review","volume":"2","author":"Juodzbalys","year":"2011","journal-title":"J. Oral Maxillofac. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1097\/00001665-200501000-00019","article-title":"Three-dimensional osteotomy planning in maxillofacial surgery including soft tissue prediction","volume":"16","author":"Westermark","year":"2005","journal-title":"J. Craniofacial Surg."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Weiss, R., and Read-Fuller, A. (2019). Cone Beam Computed Tomography in Oral and Maxillofacial Surgery: An Evidence-Based Review. Dent. J., 7.","DOI":"10.3390\/dj7020052"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3S","DOI":"10.14219\/jada.archive.2010.0359","article-title":"Operational principles for cone-beam computed tomography","volume":"141","author":"Hatcher","year":"2010","journal-title":"J. Am. Dent. Assoc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cxom.2011.12.008","article-title":"A comparison of maxillofacial CBCT and medical CT","volume":"20","author":"Angelopoulos","year":"2012","journal-title":"Atlas Oral Maxillofac. Surg. Clin. N. Am."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1016\/j.ijom.2011.02.032","article-title":"The use of cone beam CT for the removal of wisdom teeth changes the surgical approach compared with panoramic radiography: A pilot study","volume":"40","author":"Ghaeminia","year":"2011","journal-title":"Int. J. Oral Maxillofac. Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1097\/RCT.0000000000000669","article-title":"A novel registration-based semi-automatic mandible segmentation pipeline using computed tomography images to study mandibular development","volume":"42","author":"Chuang","year":"2018","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wallner, J., Hochegger, K., Chen, X., Mischak, I., Reinbacher, K., Pau, M., Zrnc, T., Schwenzer-Zimmerer, K., Zemann, W., and Schmalstieg, D. (2018). Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196378"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Issa, J., Olszewski, R., and Dyszkiewicz-Konwi\u0144ska, M. (2022). The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19010560"},{"key":"ref_12","first-page":"253","article-title":"Automatic extraction of inferior alveolar nerve canal using feature-enhancing panoramic volume rendering","volume":"58","author":"Kim","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s11548-016-1484-2","article-title":"Automatic segmentation of mandibular canal in cone beam CT images using conditional statistical shape model and fast marching","volume":"12","author":"Abdolali","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Abdolali, F., Zoroofi, R.A., and Biniaz, A. (2018, January 29\u201330). Fully automated detection of the mandibular canal in cone beam CT images using Lie group based statistical shape models. Proceedings of the 25th IEEE National and 3rd International Iranian Conference on Biomedical Engineering (ICBME), Qom, Iran.","DOI":"10.1109\/ICBME.2018.8703529"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7074","DOI":"10.1002\/mp.15274","article-title":"Inferior alveolar canal segmentation based on cone-beam computed tomography","volume":"48","author":"Wei","year":"2021","journal-title":"Med. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"16885","DOI":"10.1038\/s41598-021-96433-1","article-title":"Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning","volume":"11","author":"Lee","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12839","DOI":"10.1038\/s41598-020-69817-y","article-title":"Volumetric lung nodule segmentation using adaptive roi with multi-view residual learning","volume":"10","author":"Usman","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5711","DOI":"10.1038\/s41598-020-62586-8","article-title":"Automatic mandibular canal detection using a deep convolutional neural network","volume":"10","author":"Kwak","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-62321-3","article-title":"Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes","volume":"10","author":"Jaskari","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_21","first-page":"208","article-title":"Residual Fully Convolutional Network for Mandibular Canal Segmentation","volume":"14","author":"Faradhilla","year":"2021","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101483","DOI":"10.1109\/ACCESS.2022.3208350","article-title":"Dental-YOLO: Alveolar Bone and Mandibular Canal Detection on Cone Beam Computed Tomography Images for Dental Implant Planning","volume":"10","author":"Widiasri","year":"2022","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"198403","DOI":"10.1109\/ACCESS.2020.3035345","article-title":"Melanoma lesion detection and segmentation using YOLOv4-DarkNet and active contour","volume":"8","author":"Albahli","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","unstructured":"Dhar, M.K., and Yu, Z. (2021). Automatic tracing of mandibular canal pathways using deep learning. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103786","DOI":"10.1016\/j.jdent.2021.103786","article-title":"Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography","volume":"114","author":"Verhelst","year":"2021","journal-title":"J. Dent."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103891","DOI":"10.1016\/j.jdent.2021.103891","article-title":"Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT","volume":"116","author":"Lahoud","year":"2022","journal-title":"J. Dent."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11500","DOI":"10.1109\/ACCESS.2022.3144840","article-title":"Deep segmentation of the mandibular canal: A new 3D annotated dataset of CBCT volumes","volume":"10","author":"Cipriano","year":"2022","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cipriano, M., Allegretti, S., Bolelli, F., Pollastri, F., and Grana, C. (2022, January 18\u201324). Improving segmentation of the inferior alveolar nerve through deep label propagation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.02046"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111272","DOI":"10.1109\/ACCESS.2022.3213839","article-title":"Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention","volume":"10","author":"Du","year":"2022","journal-title":"IEEE Access"},{"key":"ref_30","unstructured":"(2022, December 14). Technology. Digital Radiographic Images in Dental Practice. Available online: https:\/\/www.lauc.net\/en\/technology\/."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1142\/S0218488598000094","article-title":"The vanishing gradient problem during learning recurrent neural nets and problem solutions","volume":"6","author":"Hochreiter","year":"1998","journal-title":"Int. J. Uncertain. Fuzziness Knowl.-Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, S., Zhang, J., Ruan, C., and Zhang, Y. (2019, January 18\u201321). Multi-Stage Attention-Unet for Wireless Capsule Endoscopy Image Bleeding Area Segmentation. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.","DOI":"10.1109\/BIBM47256.2019.8983292"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Soille, P. (2004). Erosion and Dilation. Morphological Image Analysis: Principles and Applications, Springer.","DOI":"10.1007\/978-3-662-05088-0"},{"key":"ref_35","unstructured":"Chollet, F. (2022, December 14). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_36","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2022, December 14). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9877\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:01Z","timestamp":1760146921000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":36,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249877"],"URL":"https:\/\/doi.org\/10.3390\/s22249877","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}