scholarly journals Is Manual Segmentation the Real Gold Standard for Tooth Segmentation? A Preliminary in vivo Study Using Cone-beam Computed Tomography Images

2021 ◽  
Vol 22 (3) ◽  
pp. 263-273
Author(s):  
Sercan Sabancı ◽  
Elif Şener ◽  
Rukiye Irmak Turhal ◽  
Barış Oğuz Gürses ◽  
Figen Gövsa ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Gold Standard ◽  
Cone Beam ◽  
Manual Segmentation ◽  
The Real ◽  
Computed Tomography Images ◽  
Tooth Segmentation

3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images

2016 ◽  
Vol 43 (9) ◽  
pp. 5040-5050 ◽  
Author(s):  
Yuru Pei ◽  
Xingsheng Ai ◽  
Hongbin Zha ◽  
Tianmin Xu ◽  
Gengyu Ma
Keyword(s):  
Computed Tomography ◽  
Random Walks ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Computed Tomography Images ◽  
Tooth Segmentation

scholarly journals Root canal segmentation in cone-beam computed tomography

2019 ◽  
Vol 18 ◽  
pp. e191627
Author(s):  
Juliane Freitas Machado ◽  
Paula Maciel Pires ◽  
Thais Maria Pires dos Santos ◽  
Aline de Almeida Neves ◽  
Ricardo Tadeu Lopes ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Root Canal ◽  
Gold Standard ◽  
Automatic Segmentation ◽  
Cone Beam ◽  
Micro Ct ◽  
Gold Standard Method ◽  
Computed Tomography Images ◽  
Segmentation Methods

Aim: The purpose of this study was to compare root canal volumes (RCVs) obtained by means of cone beam computed tomography (CBCT) to those obtained by micro-computed tomography (micro-CT) after applying different segmentation algorithms. Methods: Eighteen extracted human teeth with sound root canals were individually scanned in CBCT and micro-CT using specific acquisition parameters. Two different images segmentation strategies were applied to both acquisition methods (a visual and an automatic threshold). From each segmented tooth, the root canal volume was obtained. A paired t-test was used to identify differences between mean values resulted from the experimental groups and the gold standard. In addition, Pearson correlation coefficients and the agreement among the experimental groups with the gold standard were also calculated. The significance level adopted was 5%. Results: No statistical differences between the segmentation methods (visual and automatic) were observed for micro-CT acquired images. However, significant differences for the two segmentation methods tested were seen when CBCT acquired images were compared with the micro-CT automatic segmentation methods used. In general, an overestimation of the values in the visual method were observed while an underestimation was observed with the automatic segmentation algorithm. Conclusion: Cone beam computed tomography images acquired with parameters used in the present study resulted in low agreement with root canal volumes obtained with a micro-CT tomography gold standard method of RCV calculation.  

scholarly journals A Deep Learning Approach For Dental Implant Planning On Cone-Beam Computed Tomography Images

2021 ◽  
Author(s):  
Sevda Kurt Bayrakdar ◽  
Kaan Orhan ◽  
Ibrahim Sevki Bayrakdar ◽  
Elif Bilgir ◽  
Matvey Ezhov ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Rank Test ◽  
Human Observer ◽  
Manual Segmentation ◽  
Support Mechanism ◽  
Computed Tomography Images ◽  
Bone Height ◽  
Missing Tooth

Abstract Background: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images.Methods: Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual segmentation method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. After all of this evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc.) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual segmentation and AI methods were compared using Bland-Altman analysis and Wilcoxon signed rank test.Results: In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p>0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p<0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions.Conclusions: Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

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