[1] 宿玉成. 浅谈数字化口腔种植治疗[J].中华口腔医学杂志, 2016,51(4):194-200. DOI: 10.3760/cma.j.issn. 1002-0098. 2016.04.002. [2] Wallner J, Mischak I, Egger J.Computed tomography data collection of the complete human mandible and valid clinical ground truth models[J]. Sci Data, 2019,6:190003. DOI: 10.1038/sdata.2019.3. [3] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015,521(7553):436-444. DOI: 10.1038/nature14539. [4] Kurt Bayrakdar S, Orhan K, Bayrakdar IS, et al.A deep learning approach for dental implant planning in cone-beam computed tomography images[J]. BMC Med Imaging, 2021,21(1):86. DOI: 10.1186/s12880-021-00618-z. [5] Huang N, Liu P, Yan Y, et al.Predicting the risk of dental implant loss using deep learning[J]. J Clin Periodontol, 2022,49(9):872-883. DOI: 10.1111/jcpe.13689. [6] Huang Z, Zheng H, Huang J, et al.The construction and evaluation of a multi-task convolutional neural network for a cone-beam computed-tomography-based assessment of implant stability[J]. Diagnostics (Basel), 2022,12(11):2673. DOI: 10.3390/diagnostics12112673. [7] Sakai T, Li H, Shimada T, et al.Development of artificial intelligence model for supporting implant drilling protocol decision making[J]. J Prosthodont Res, 2023,67(3):360-365. DOI: 10.2186/jpr.JPR_D_22_00053. [8] Pham DL, Xu C, Prince JL.Current methods in medical image segmentation[J]. Annu Rev Biomed Eng, 2000,2:315-337. DOI: 10.1146/annurev.bioeng.2.1.315. [9] Keceli HG, Dursun E, Dolgun A, et al.Evaluation of single tooth loss to maxillary sinus and surrounding bone anatomy with cone-beam computed tomography: a multicenter study[J]. Implant Dent, 2017,26(5):690-699. DOI: 10.1097/ID.0000000000000652. [10] Fontenele RC, Gerhardt M, Picoli FF, et al.Convolutional neural network-based automated maxillary alveolar bone segmentation on cone-beam computed tomography images[J]. Clin Oral Implants Res, 2023,34(6):565-574. DOI: 10.1111/clr.14063. [11] Qiu B, van der Wel H, Kraeima J, et al. Robust and accurate mandible segmentation on dental CBCT scans affected by metal artifacts using a prior shape model[J]. J Pers Med, 2021,11(5):364. DOI: 10.3390/jpm11050364. [12] Minnema J, van Eijnatten M, Hendriksen AA, et al. Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network[J]. Med Phys, 2019,46(11):5027-5035. DOI: 10.1002/mp.13793. [13] Preda F, Morgan N, Van Gerven A, et al.Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography:a validation study[J]. J Dent, 2022,124:104238. DOI: 10.1016/j.jdent.2022.104238. [14] Cui Z, Li C, Wang W.ToothNet: automatic tooth instance segmentation and identification from cone beam CT images[C]. California: IEEE, CVF Conference on Computer Vision and Pattern Recognition, 2019: 6368-6377. DOI:10.1109/cvpr.2019.00653. [15] Li Q, Chen K, Han L, et al.Automatic tooth roots segmentation of cone beam computed tomography image sequences using u-net and RNN[J]. J Xray Sci Technol, 2020,28(5):905-922. DOI: 10.3233/XST-200678. [16] Xu Z, Che S, Zhou Z.Research on image segmentation based on CBCT roots[C]. Singapore: Springer,Proceedings of 2019 Chinese Intelligent Systems Conference, 2020: 330-339. DOI: 10.1007/978-981-32-9682-4_34. [17] Lee S, Woo S, Yu J, et al.Automated CNN-based tooth segmentation in cone-beam CT for dental implant planning[J]. IEEE Access, 2020, 8: 50507-50518. DOI: 10.1109/ACCESS.2020.2975826. [18] Cui Z, Fang Y, Mei L, et al.A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images[J]. Nat Commun, 2022,13(1):2096. DOI: 10.1038/s41467-022-29637-2. [19] Kwak GH, Kwak EJ, Song JM, et al.Automatic mandibular canal detection using a deep convolutional neural network[J]. Sci Rep, 2020,10(1):5711. DOI: 10.1038/s41598-020-62586-8. [20] Jaskari J, Sahlsten J, Järnstedt J, et al.Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes[J]. Sci Rep, 2020,10(1):5842. DOI: 10.1038/s41598-020-62321-3. [21] Lahoud P, Diels S, Niclaes L, et al.Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT[J]. J Dent, 2022,116:103891. DOI: 10.1016/j.jdent.2021.103891. [22] Jeoun BS, Yang S, Lee SJ, et al.Canal-net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network[J]. Sci Rep, 2022,12(1):13460. DOI: 10.1038/s41598-022-17341-6. [23] Du G, Tian X, Song Y.Mandibular canal segmentation from CBCT image using 3D convolutional neural network with scSE attention[J]. IEEE Access, 2022, 10: 111272-111283. DOI:10.1109/ACCESS.2022.3213839. [24] Morgan N, Van Gerven A, Smolders A, et al.Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images[J]. Sci Rep, 2022,12(1):7523. DOI: 10.1038/s41598-022-11483-3. [25] Choi H, Jeon KJ, Kim YH, et al.Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images[J]. Sci Rep, 2022,12(1):14009. DOI: 10.1038/s41598-022-18436-w. [26] Jung SK, Lim HK, Lee S, et al.Deep active learning for automatic segmentation of maxillary sinus lesions using a convolutional neural network[J]. Diagnostics (Basel), 2021,11(4):688. DOI: 10.3390/diagnostics11040688. [27] Hung KF, Ai Q, King AD, et al.Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network[J]. Clin Oral Investig, 2022,26(5):3987-3998. DOI: 10.1007/s00784-021-04365-x. [28] Singh A, Sengupta S, Lakshminarayanan V.Explainable deep learning models in medical image analysis[J]. J Imaging, 2020,6(6):52. DOI: 10.3390/jimaging6060052. |