Journal
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING
Volume 35, Issue 10, Pages -Publisher
WILEY
DOI: 10.1002/cnm.3241
Keywords
convolutional neural network; dense condition random field; spare octree; tooth preparation margin line
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Funding
- Jiangsu Province science and technology support plan project, China [BE2018010-2]
- Six talent peaks project in Jiangsu Province, China [GDZB-034]
- Natural Science Foundation of Jiangsu Province, China [BK20161487]
- National Natural Science Foundation of China [51775273]
- National Key R&D Program of China [2018YFB1106903]
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The tooth preparation margin line has a significant impact on the marginal fitness for dental restoration. Among the previous methods, the extraction of margin line mainly relies on manual interaction, which is complicated and inefficient. Therefore, we propose a method to extract the margin line with the convolutional neural network based on sparse octree (S-Octree) structure. First, the dental preparations are rotated to augment the dataset. Second, the preparation models are treated as the sparse point cloud with labels through the spatial partition method of the S-Octree. Then, based on the feature line, the dental preparation point cloud is automatically divided into two regions by the convolutional neural network (CNN). Third, in order to obtain the margin line, we adopt some methods such as the dense condition random field (dense CRF), point cloud reconstruction, and back projection to the original dental preparation model. Finally, based on the measurement indicators of accuracy, sensitivity, and specificity, the average accuracy of the label predicted by the network model can reach 97.43%. The experimental results show that our method can automatically accomplish the extraction of the tooth preparation margin line.
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