分割
计算机科学
人工智能
锥束ct
牙槽
牙骨质
牙科
图像分割
锥束ct
计算机视觉
生物医学工程
牙本质
医学
计算机断层摄影术
放射科
作者
Haofan Yang,Xinwen Wang,Gang Li
出处
期刊:International Journal of Morphology
日期:2022-01-01
卷期号:40 (2): 407-413
被引量:1
标识
DOI:10.4067/s0717-95022022000200407
摘要
This study aims to extract teeth and alveolar bone structures in CBCT images automatically, which is a key step in CBCT image analysis in the field of stomatology. In this study, semantic segmentation was used for automatic segmentation. Five marked classes of CBCT images were input for U-net neural network training. Tooth hard tissue (including enamel, dentin, and cementum), dental pulp cavity, cortical bone, cancellous bone, and other tissues were marked manually in each class. The output data were from different regions of interest. The network configuration and training parameters were optimized and adjusted according to the prediction effect. This method can be used to segment teeth and peripheral bone structures using CBCT. The time of the automatic segmentation process for each CBCT was less than 13 min. The Dice of the evaluation reference image was 98 %. The U-net model combined with the watershed method can effectively segment the teeth, pulp cavity, and cortical bone in CBCT images. It can provide morphological information for clinical treatment.
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