141 related articles for article (PubMed ID: 31016947)
1. [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]
2. Evaluation of the accuracy of cone-beam computed tomography image segmentation of isolated tooth roots based on the dynamic threshold method.
Su S; Liu YM; Zhan LP; Gao SY; He C; Zhang Q; Huang XF
BMC Oral Health; 2023 Oct; 23(1):752. PubMed ID: 37833773
[TBL] [Abstract][Full Text] [Related]
3. Automatic tooth roots segmentation of cone beam computed tomography image sequences using U-net and RNN.
Li Q; Chen K; Han L; Zhuang Y; Li J; Lin J
J Xray Sci Technol; 2020; 28(5):905-922. PubMed ID: 32986647
[TBL] [Abstract][Full Text] [Related]
4. 3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images.
Pei Y; Ai X; Zha H; Xu T; Ma G
Med Phys; 2016 Sep; 43(9):5040. PubMed ID: 27587034
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Comparison of an adaptive local thresholding method on CBCT and µCT endodontic images.
Michetti J; Basarab A; Diemer F; Kouame D
Phys Med Biol; 2017 Dec; 63(1):015020. PubMed ID: 28976357
[TBL] [Abstract][Full Text] [Related]
8. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model.
Gan Y; Xia Z; Xiong J; Zhao Q; Hu Y; Zhang J
Med Phys; 2015 Jan; 42(1):14-27. PubMed ID: 25563244
[TBL] [Abstract][Full Text] [Related]
9. Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images.
Liu Y; Xie R; Wang L; Liu H; Liu C; Zhao Y; Bai S; Liu W
Int J Oral Sci; 2024 May; 16(1):34. PubMed ID: 38719817
[TBL] [Abstract][Full Text] [Related]
10. Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation.
Chung M; Lee M; Hong J; Park S; Lee J; Lee J; Yang IH; Lee J; Shin YG
Comput Biol Med; 2020 May; 120():103720. PubMed ID: 32250852
[TBL] [Abstract][Full Text] [Related]
11. A new method to measure mesiodistal angulation and faciolingual inclination of each whole tooth with volumetric cone-beam computed tomography images.
Tong H; Enciso R; Van Elslande D; Major PW; Sameshima GT
Am J Orthod Dentofacial Orthop; 2012 Jul; 142(1):133-43. PubMed ID: 22748999
[TBL] [Abstract][Full Text] [Related]
12. Accurate tooth segmentation with improved hybrid active contour model.
Wang Y; Liu S; Wang G; Liu Y
Phys Med Biol; 2018 Dec; 64(1):015012. PubMed ID: 30524079
[TBL] [Abstract][Full Text] [Related]
13. [Segmentation and accuracy validation of mandibular molar and pulp cavity on cone-beam CT images by U-net neural network].
Lin X; Fu YJ; Ren GQ; Wen JH; Chen YF; Zhang Q
Shanghai Kou Qiang Yi Xue; 2022 Oct; 31(5):454-459. PubMed ID: 36758590
[TBL] [Abstract][Full Text] [Related]
14. [A method for rapid extracting three-dimensional root model of vivo tooth from cone beam computed tomography data based on the anatomical characteristics of periodontal ligament].
Zhao YJ; Wang SW; Liu Y; Wang Y
Beijing Da Xue Xue Bao Yi Xue Ban; 2017 Feb; 49(1):54-9. PubMed ID: 28203004
[TBL] [Abstract][Full Text] [Related]
15. Refined tooth and pulp segmentation using U-Net in CBCT image.
Duan W; Chen Y; Zhang Q; Lin X; Yang X
Dentomaxillofac Radiol; 2021 Sep; 50(6):20200251. PubMed ID: 33444070
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Influence of dental fillings and tooth type on the performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images - A validation study.
Fontenele RC; Gerhardt MDN; Pinto JC; Van Gerven A; Willems H; Jacobs R; Freitas DQ
J Dent; 2022 Apr; 119():104069. PubMed ID: 35183696
[TBL] [Abstract][Full Text] [Related]
18. Monitoring of typodont root movement via crown superimposition of single cone-beam computed tomography and consecutive intraoral scans.
Lee RJ; Pham J; Choy M; Weissheimer A; Dougherty HL; Sameshima GT; Tong H
Am J Orthod Dentofacial Orthop; 2014 Mar; 145(3):399-409. PubMed ID: 24582031
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis.
Lin Y; He M
J Healthc Eng; 2021; 2021():4676316. PubMed ID: 34594483
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]