{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:32:34Z","timestamp":1783096354399,"version":"3.54.6"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721106","type":"print"},{"value":"9783031721113","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72111-3_2","type":"book-chapter","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:01:34Z","timestamp":1728162094000},"page":"14-23","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automatic Mandibular Semantic Segmentation of\u00a0Teeth Pulp Cavity and\u00a0Root Canals, and\u00a0Inferior Alveolar Nerve on\u00a0Pulpy3D Dataset"],"prefix":"10.1007","author":[{"given":"Mahmoud","family":"Gamal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marwa","family":"Baraka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marwan","family":"Torki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"97296","DOI":"10.1109\/ACCESS.2020.2991799","volume":"8","author":"Y Chen","year":"2020","unstructured":"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\u201397309 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2991799","journal-title":"IEEE Access"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Cipriano, M., et al.: Deep segmentation of the mandibular canal: a new 3D annotated dataset of CBCT volumes. IEEE Access 10, 11500\u201311510 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3144840","DOI":"10.1109\/ACCESS.2022.3144840"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Cipriano, M., Allegretti, S., Bolelli, F., Pollastri, F., Grana, C.: Improving segmentation of the inferior alveolar nerve through deep label propagation (2022). https:\/\/ditto.ing.unimore.it\/maxillo\/","DOI":"10.1109\/CVPR52688.2022.02046"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Deng, D.: DBSCAN clustering algorithm based on density, pp. 949\u2013953. Institute of Electrical and Electronics Engineers Inc. (2020). https:\/\/doi.org\/10.1109\/IFEEA51475.2020.00199","DOI":"10.1109\/IFEEA51475.2020.00199"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Duan, W., Chen, Y., Zhang, Q., Lin, X., Yang, X.: Refined tooth and pulp segmentation using U-net in CBCT image. Dentomaxillofac. Radiol. 50(6), 20200251 (2021). https:\/\/doi.org\/10.1259\/dmfr.20200251","DOI":"10.1259\/dmfr.20200251"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Gan, Y., Xia, Z., Xiong, J., Zhao, Q., Hu, Y., Zhang, J.: Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Med. Phys. 42, 14\u201327 (2015). https:\/\/doi.org\/10.1118\/1.4901521","DOI":"10.1118\/1.4901521"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Gao, H., Chae, O.: Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recogn. 43, 2406\u20132417 (2010). https:\/\/doi.org\/10.1016\/j.patcog.2010.01.010","DOI":"10.1016\/j.patcog.2010.01.010"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: Transformers for 3D medical image segmentation (2021). http:\/\/arxiv.org\/abs\/2103.10504","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"2_CR9","unstructured":"Lee, J., Chung, M., Lee, M., Shin, Y.G.: Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement (2021). http:\/\/arxiv.org\/abs\/2102.01315"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Lin, X., et al.: Micro-computed tomography-guided artificial intelligence for pulp cavity and tooth segmentation on cone-beam computed tomography. J. Endod. 47, 1933\u20131941 (2021). https:\/\/doi.org\/10.1016\/j.joen.2021.09.001","DOI":"10.1016\/j.joen.2021.09.001"},{"key":"2_CR11","unstructured":"Liu, Y., Xin, R., Yang, T., Wang, L.: Inferior alveolar nerve segmentation in CBCT images using connectivity-based selective re-training (2023). http:\/\/arxiv.org\/abs\/2308.09298"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation (2016). http:\/\/arxiv.org\/abs\/1606.04797","DOI":"10.1109\/3DV.2016.79"},{"key":"2_CR13","unstructured":"Oktay, O., et al.: Attention U-Net: Learning where to look for the pancreas (2018). http:\/\/arxiv.org\/abs\/1804.03999"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation (2015). http:\/\/arxiv.org\/abs\/1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Wang, H., Minnema, J., Batenburg, K.J., Forouzanfar, T., Hu, F.J., Wu, G.: Multiclass CBCT image segmentation for orthodontics with deep learning. J. Dent. Res. 100, 943\u2013949 (2021). https:\/\/doi.org\/10.1177\/00220345211005338","DOI":"10.1177\/00220345211005338"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Wang, L., peng Li, J., pu\u00a0Ge, Z., Li, G.: CBCT image based segmentation method for tooth pulp cavity region extraction. Dentomaxillofac. Radiol. 48(2), 20180236 (2019). https:\/\/doi.org\/10.1259\/dmfr.20180236","DOI":"10.1259\/dmfr.20180236"},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Wang, Y., 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. 85, 102750 (2023). https:\/\/doi.org\/10.1016\/j.media.2023.102750","DOI":"10.1016\/j.media.2023.102750"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Xia, Z., Gan, Y., Chang, L., Xiong, J., Zhao, Q.: Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth. Comput. Methods Programs Biomed. 138, 1\u201312 (2017). https:\/\/doi.org\/10.1016\/j.cmpb.2016.10.002","DOI":"10.1016\/j.cmpb.2016.10.002"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Yang, H., Wang, X., Li, G., Yang, H..: Tooth and pulp chamber automatic segmentation with artificial intelligence network and morphometry method in cone-beam CT segmentaci\u00f3n autom\u00e1tica de c\u00e1maras dentales y pulpares con red de inteligencia artificial y m\u00e9todo de morfometr\u00eda en tc de haz c\u00f3nico (2022)","DOI":"10.4067\/S0717-95022022000200407"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Yang, S., et al.: Automatic segmentation of inferior alveolar canal with ambiguity classification in panoramic images using deep learning. Heliyon 9(2), e13694 (2023). https:\/\/doi.org\/10.1016\/j.heliyon.2023.e13694","DOI":"10.1016\/j.heliyon.2023.e13694"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Yau, H.T., Yang, T.J., Chen, Y.C.: Tooth model reconstruction based upon data fusion for orthodontic treatment simulation. Comput. Biol. Med. 48, 8\u201316 (2014). https:\/\/doi.org\/10.1016\/j.compbiomed.2014.02.001","DOI":"10.1016\/j.compbiomed.2014.02.001"},{"issue":"3","key":"2_CR22","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116\u20131128 (2006)","journal-title":"Neuroimage"},{"key":"2_CR23","doi-asserted-by":"publisher","unstructured":"Zheng, Q., Ge, Z., Du, H., Li, G.: Age estimation based on 3d pulp chamber segmentation of first molars from cone-beam-computed tomography by integrated deep learning and level set (2021). https:\/\/doi.org\/10.1007\/s00414-020","DOI":"10.1007\/s00414-020"},{"key":"2_CR24","doi-asserted-by":"publisher","unstructured":"Zheng, Z., Yan, H., Setzer, F.C., Shi, K.J., Mupparapu, M., Li, J.: Anatomically constrained deep learning for automating dental CBCT segmentation and lesion detection. IEEE Trans. Autom. Sci. Eng. 18, 603\u2013614 (2021). https:\/\/doi.org\/10.1109\/TASE.2020.3025871","DOI":"10.1109\/TASE.2020.3025871"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72111-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:02:02Z","timestamp":1728162122000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72111-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721106","9783031721113"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72111-3_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}