Tooth segmentation and identification on cone-beam computed tomography with convolutional neural network based on spatial embedding information
10.19723/j.issn.1671-167X.2024.04.030
- VernacularTitle:基于卷积神经网络实现锥形束CT牙齿分割及牙位标定
- Author:
Shishi BO
1
,
2
;
Chengzhi GAO
Author Information
1. 北京大学口腔医学院·口腔医院综合二科,国家口腔医学中心,国家口腔疾病临床医学研究中心,口腔生物材料和数字诊疗装备国家工程研究中心,北京 100081
2. 北京大学人民医院口腔科,北京 100044
- Keywords:
Convolutional neural network;
Cone-beam computed tomography;
Tooth instance segmen-tation;
Tooth identification
- From:
Journal of Peking University(Health Sciences)
2024;56(4):735-740
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To propose a novel neural network to achieve tooth instance segmentation and recognition based on cone-beam computed tomography(CBCT)voxel data.Methods:The proposed methods included three different convolutional neural network models.The architecture was based on the Resnet module and built according to the structure of"Encoder-Decoder"and U-Net.The CBCT image was de-sampled and a fixed-size region of interest(ROI)containing all the teeth was determined.ROI would first through a two-branch"encoder and decoder"structure of the network,the network could pre-dict each voxel in the input data of the spatial embedding.The post-processing algorithm would cluster the prediction results of the relevant spatial location information according to the two-branch network to realize the tooth instance segmentation.The tooth position identification was realized by another U-Net model based on the multi-classification segmentation task.According to the predicted results of the net-work,the post-processing algorithm would classify the tooth position according to the voting results of each tooth instance segmentation.At the original spatial resolution,a U-Net network model for the fine-tooth segmentation was trained using the region corresponding to each tooth as the input.According to the results of instance segmentation and tooth position identification,the model would process the correspon-ding positions on the high-resolution CBCT images to obtain the high-resolution tooth segmentation re-sults.In this study,CBCT data of 59 cases with simple crown prostheses and implants were collected for manual labeling as the database,and statistical indicators were evaluated for the prediction results of the algorithm.To assess the performance of tooth segmentation and classification,instance Dice similarity co-efficient(IDSC)and the average Dice similarity coefficient(ADSC)were calculated.Results:The ex-perimental results showed that the IDSC was 89.35％,and the ADSC was 84.74％.After eliminating the data with prostheses artifacts,the database of 43 samples was generated,and the performance of the training network was better,with 90.34％for IDSC and 87.88％for ADSC.The framework achieved ex-cellent performance on tooth segmentation and identification.Voxels near intercuspation surfaces and fuzzy boundaries could be separated into correct instances by this framework.Conclusion:The results show that this method can not only successfully achieve 3D tooth instance segmentation but also identify all teeth notation numbers accurately,which has clinical practicability.
