Panoramic radiographs can assist dentist to quickly evaluate patients’overall oral health status.The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify patholog...Panoramic radiographs can assist dentist to quickly evaluate patients’overall oral health status.The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology,and also plays a key role in an automatic diagnosis system.However,the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist,while the interpretation of panoramic radiographs might lead misdiagnosis.Therefore,it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs.In this study,SWin-Unet,the transformer-based Ushaped encoder-decoder architecture with skip-connections,is introduced to perform panoramic radiograph segmentation.To well evaluate the tooth segmentation performance of SWin-Unet,the PLAGH-BH dataset is introduced for the research purpose.The performance is evaluated by F1 score,mean intersection and Union(IoU)and Acc,Compared with U-Net,Link-Net and FPN baselines,SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset.These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation,and is valuable for the potential clinical application.展开更多
针对当前的研究方法在牙齿全景X光片上提取的信息较为单一,而未曾考虑将牙齿的类别信息与形状位置信息融合提取的问题,提出一种实例分割方法同时实现牙齿识别与分割。主要通过融合跳跃结构和SE(Squeeze and Excitation)模块对Mask R-CN...针对当前的研究方法在牙齿全景X光片上提取的信息较为单一,而未曾考虑将牙齿的类别信息与形状位置信息融合提取的问题,提出一种实例分割方法同时实现牙齿识别与分割。主要通过融合跳跃结构和SE(Squeeze and Excitation)模块对Mask R-CNN实例分割模型中的分割分支进行改进,并以牙齿功能与FDI牙位两种类别编码方式,采用400张牙齿全景X光片数据进行实验仿真。实验结果表明改进后的模型相比于其他模型,可以同时有效地进行牙齿分类和分割,实现牙齿类别、形状、位置信息的融合提取,改善了Mask R-CNN实例分割模型在分割分支中语义信息提取不足的问题。展开更多
文摘Panoramic radiographs can assist dentist to quickly evaluate patients’overall oral health status.The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology,and also plays a key role in an automatic diagnosis system.However,the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist,while the interpretation of panoramic radiographs might lead misdiagnosis.Therefore,it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs.In this study,SWin-Unet,the transformer-based Ushaped encoder-decoder architecture with skip-connections,is introduced to perform panoramic radiograph segmentation.To well evaluate the tooth segmentation performance of SWin-Unet,the PLAGH-BH dataset is introduced for the research purpose.The performance is evaluated by F1 score,mean intersection and Union(IoU)and Acc,Compared with U-Net,Link-Net and FPN baselines,SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset.These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation,and is valuable for the potential clinical application.
文摘针对当前的研究方法在牙齿全景X光片上提取的信息较为单一,而未曾考虑将牙齿的类别信息与形状位置信息融合提取的问题,提出一种实例分割方法同时实现牙齿识别与分割。主要通过融合跳跃结构和SE(Squeeze and Excitation)模块对Mask R-CNN实例分割模型中的分割分支进行改进,并以牙齿功能与FDI牙位两种类别编码方式,采用400张牙齿全景X光片数据进行实验仿真。实验结果表明改进后的模型相比于其他模型,可以同时有效地进行牙齿分类和分割,实现牙齿类别、形状、位置信息的融合提取,改善了Mask R-CNN实例分割模型在分割分支中语义信息提取不足的问题。
