357 related articles for article (PubMed ID: 32250852)
21. Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning.
Wang H; Minnema J; Batenburg KJ; Forouzanfar T; Hu FJ; Wu G
J Dent Res; 2021 Aug; 100(9):943-949. PubMed ID: 33783247
[TBL] [Abstract][Full Text] [Related]
22. [A tooth cone beam computer tomography image segmentation method based on the local Gaussian distribution fitting].
Liu S; Wang Y
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2019 Apr; 36(2):291-297. PubMed ID: 31016947
[TBL] [Abstract][Full Text] [Related]
23. A level-set based approach for anterior teeth segmentation in cone beam computed tomography images.
Ji DX; Ong SH; Foong KW
Comput Biol Med; 2014 Jul; 50():116-28. PubMed ID: 24853776
[TBL] [Abstract][Full Text] [Related]
24. Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization.
Zhang J; Liu M; Wang L; Chen S; Yuan P; Li J; Shen SG; Tang Z; Chen KC; Xia JJ; Shen D
Med Image Anal; 2020 Feb; 60():101621. PubMed ID: 31816592
[TBL] [Abstract][Full Text] [Related]
25. A progressive framework for tooth and substructure segmentation from cone-beam CT images.
Tan M; Cui Z; Zhong T; Fang Y; Zhang Y; Shen D
Comput Biol Med; 2024 Feb; 169():107839. PubMed ID: 38150887
[TBL] [Abstract][Full Text] [Related]
26. 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks.
Xu X; Liu C; Zheng Y
IEEE Trans Vis Comput Graph; 2019 Jul; 25(7):2336-2348. PubMed ID: 29994311
[TBL] [Abstract][Full Text] [Related]
27. Automatic Registration Between Dental Cone-Beam CT and Scanned Surface via Deep Pose Regression Neural Networks and Clustered Similarities.
Chung M; Lee J; Song W; Song Y; Yang IH; Lee J; Shin YG
IEEE Trans Med Imaging; 2020 Dec; 39(12):3900-3909. PubMed ID: 32746134
[TBL] [Abstract][Full Text] [Related]
28. A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study.
Shaheen E; Leite A; Alqahtani KA; Smolders A; Van Gerven A; Willems H; Jacobs R
J Dent; 2021 Dec; 115():103865. PubMed ID: 34710545
[TBL] [Abstract][Full Text] [Related]
29. Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography:A validation study.
Preda F; Morgan N; Van Gerven A; Nogueira-Reis F; Smolders A; Wang X; Nomidis S; Shaheen E; Willems H; Jacobs R
J Dent; 2022 Sep; 124():104238. PubMed ID: 35872223
[TBL] [Abstract][Full Text] [Related]
30. Micro-Computed Tomography-Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography.
Lin X; Fu Y; Ren G; Yang X; Duan W; Chen Y; Zhang Q
J Endod; 2021 Dec; 47(12):1933-1941. PubMed ID: 34520812
[TBL] [Abstract][Full Text] [Related]
31. CBCT image based segmentation method for tooth pulp cavity region extraction.
Wang L; Li JP; Ge ZP; Li G
Dentomaxillofac Radiol; 2019 Feb; 48(2):20180236. PubMed ID: 30216093
[TBL] [Abstract][Full Text] [Related]
32. Dual-energy-based metal segmentation for metal artifact reduction in dental computed tomography.
Hegazy MAA; Eldib ME; Hernandez D; Cho MH; Cho MH; Lee SY
Med Phys; 2018 Feb; 45(2):714-724. PubMed ID: 29220087
[TBL] [Abstract][Full Text] [Related]
33. Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography.
Lahoud P; EzEldeen M; Beznik T; Willems H; Leite A; Van Gerven A; Jacobs R
J Endod; 2021 May; 47(5):827-835. PubMed ID: 33434565
[TBL] [Abstract][Full Text] [Related]
34. Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images.
Heutink F; Koch V; Verbist B; van der Woude WJ; Mylanus E; Huinck W; Sechopoulos I; Caballo M
Comput Methods Programs Biomed; 2020 Jul; 191():105387. PubMed ID: 32109685
[TBL] [Abstract][Full Text] [Related]
35. Self-contained deep learning-based boosting of 4D cone-beam CT reconstruction.
Madesta F; Sentker T; Gauer T; Werner R
Med Phys; 2020 Nov; 47(11):5619-5631. PubMed ID: 33063329
[TBL] [Abstract][Full Text] [Related]
36. Domain adversarial networks and intensity-based data augmentation for male pelvic organ segmentation in cone beam CT.
Brion E; Léger J; Barragán-Montero AM; Meert N; Lee JA; Macq B
Comput Biol Med; 2021 Apr; 131():104269. PubMed ID: 33639352
[TBL] [Abstract][Full Text] [Related]
37. Dental Segmentation in Cone-beam Computed Tomography Images Using Watershed and Morphology Operators.
Kakehbaraei S; Seyedarabi H; Zenouz AT
J Med Signals Sens; 2018; 8(2):119-124. PubMed ID: 29928637
[TBL] [Abstract][Full Text] [Related]
38. Deep learning-based tooth segmentation methods in medical imaging: A review.
Chen X; Ma N; Xu T; Xu C
Proc Inst Mech Eng H; 2024 Feb; 238(2):115-131. PubMed ID: 38314788
[TBL] [Abstract][Full Text] [Related]
39. An enhanced tooth segmentation and numbering according to FDI notation in bitewing radiographs.
Yaren Tekin B; Ozcan C; Pekince A; Yasa Y
Comput Biol Med; 2022 Jul; 146():105547. PubMed ID: 35544975
[TBL] [Abstract][Full Text] [Related]
40. Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images.
Morgan N; Van Gerven A; Smolders A; de Faria Vasconcelos K; Willems H; Jacobs R
Sci Rep; 2022 May; 12(1):7523. PubMed ID: 35525857
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
