Page 34 - 南京医科大学自然版

第45卷第12期
·1716 · 南 京 医 科 大 学 学 报 2025年12月

2096 ［30］ILESAN R R，BEYER M，KUNZ C，et al. Comparison of
［18］WANG H，MINNEMA J，BATENBURG K J，et al. Multi⁃ artificial intelligence ⁃ based applications for mandible
class CBCT image segmentation for orthodontics with segmentation：from established platforms to in ⁃ house ⁃
deep learning［J］. J Dent Res，2021，100（9）：943-949 developed software［J］. Bioengineering（Basel），2023，10
［19］SHAHEEN E，LEITE A，ALQAHTANI K A，et al. A novel （5）：604
deep learning system for multi ⁃ class tooth segmentation ［31］ LO GIUDICE A，RONSIVALLE V，SPAMPINATO C，et
and classification on cone beam computed tomography. A al. Fully automatic segmentation of the mandible based
validation study［J］. J Dent，2021，115：103865 on convolutional neural networks（CNNs）［J］. Orthod
［20］GILLOT M，BAQUERO B，LE C L，et al. Automatic multi⁃ Craniofac Res，2021，24（Suppl 2）：100-107
anatomical skull structure segmentation of cone ⁃ beam ［32］ QIU B J，VAN DER WEL H，KRAEIMA J，et al. Mandible
computed tomography scans using 3D UNETR［J］. PLoS segmentation of dental CBCT scans affected by metal
One，2022，17（10）：e0275033 artifacts using coarse⁃to⁃fine learning model［J］. J Pers
［21］ZHANG S，TONG H H，XU J J，et al. Graph convolutional Med，2021，11（6）：560
networks：a comprehensive review［J］. Comput Soc Netw， ［33］CHOI H，JEON K J，KIM Y H，et al. Deep learning⁃based
2019，6（1）：11 fully automatic segmentation of the maxillary sinus on
［22］MIKI Y，MURAMATSU C，HAYASHI T，et al. Classifica⁃ cone ⁃ beam computed tomographic images［J］. Sci Rep，
tion of teeth in cone⁃beam CT using deep convolutional 2022，12：14009
neural network［J］. Comput Biol Med，2017，80：24-29 ［34］ MORGAN N，VAN GERVEN A，SMOLDERS A，et al.
［23］LEE S，WOO S，YU J，et al. Automated CNN⁃based tooth Convolutional neural network for automatic maxillary
segmentation in cone ⁃ beam CT for dental implant sinus segmentation on cone⁃beam computed tomographic
planning［J］. IEEE Access，2020，8：50507-50518 images［J］. Sci Rep，2022，12（1）：7523
［24］ ALQUTAIBI A Y，ALGABRI R，IBRAHIM W I，et al. ［35］NI F D，XU Z N，LIU M Q，et al. Towards clinically appli⁃
Dental implant planning using artificial intelligence：a cable automated mandibular canal segmentation on CBCT
systematic review and meta⁃analysis［J］. J Prosthet Dent， ［J］. J Dent，2024，144：104931
2025，134（5）：1619-1629 ［36］LIN X，XIN W N，HUANG J N，et al. Accurate mandibu⁃
［25］KURT BAYRAKDAR S，ORHAN K，BAYRAKDAR I S， lar canal segmentation of dental CBCT using a two⁃stage
et al. A deep learning approach for dental implant 3D ⁃ UNet based segmentation framework［J］. BMC Oral
planning in cone⁃beam computed tomography images［J］. Health，2023，23（1）：551
BMC Med Imaging，2021，21（1）：86 ［37］ USMAN M，REHMAN A，SALEEM A M，et al. Dual ⁃
［26］ WIDIASRI M，ARIFIN A Z，SUCIATI N，et al. Dental⁃ stage deeply supervised attention ⁃ based convolutional
YOLO：alveolar bone and mandibular canal detection on neural networks for mandibular canal segmentation in
cone beam computed tomography images for dental CBCT scans［J］. Sensors（Basel），2022，22（24）：9877
implant planning［J］. IEEE Access，2022，10：101483- ［38］ BRANDENBURG L S，SCHLAGER S，HARZIG L S，
101494 et al. A novel method for digital reconstruction of the
［27］LIN Y X，SHI M R，XIANG D W，et al. Construction of an mucogingival borderline in optical scans of dental plaster
end⁃to⁃end regression neural network for the determina⁃ casts［J］. J Clin Med，2022，11（9）：2383
tion of a quantitative index sagittal root inclination［J］. J ［39］ YANG M，LI C S，YANG W，et al. Accurate gingival
Periodontol，2022，93（12）：1951-1960 segmentation from 3D images with artificial intelligence：
［28］ PREDA F，MORGAN N，VAN GERVEN A，et al. Deep an animal pilot study［J］. Prog Orthod，2023，24（1）：14
convolutional neural network⁃based automated segmenta⁃ ［40］ LIU Z Z，HE X X，WANG H L，et al. Hierarchical
tion of the maxillofacial complex from cone⁃beam computed self⁃supervised learning for 3D tooth segmentation in intra⁃
tomography：a validation study［J］. J Dent，2022，124： oral mesh scans［J］. IEEE Trans Med Imag，2023，42（2）：
104238 467-480
［29］ NOGUEIRA ⁃ REIS F，MORGAN N，NOMIDIS S，et al. ［41］KIM M，CHUNG M，SHIN Y G，et al. Automatic registra⁃
Three ⁃ dimensional maxillary virtual patient creation by tion of dental CT and 3D scanned model using deep split
convolutional neural network ⁃ based segmentation on jaw and surface curvature［J］. Comput Methods Programs
cone ⁃ beam computed tomography images［J］. Clin Oral Biomed，2023，233：107467
Investig，2023，27（3）：1133-1141 ［42］ELGARBA B M，FONTENELE R C，ALI S，et al. Valida⁃