In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or ove...In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.展开更多
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
Segmentation of a complete set of teeth from three-dimensional(3D)intra-oral scanner images is a crucial step in tooth identification procedures.In large-scale disasters with many victims,teeth are often the preferred...Segmentation of a complete set of teeth from three-dimensional(3D)intra-oral scanner images is a crucial step in tooth identification procedures.In large-scale disasters with many victims,teeth are often the preferred and reliable source for victim identification due to their hard and non-deformable characteristics.In this paper we present a study on the automatic segmentation of a complete set of teeth from intra-oral scanner images.We propose a tooth segmentation method based on an improved PointNet++architecture.To address the problem of inadequate segmentation capability of the teeth-gingival boundary of PointNet++,we introduce a single-point preliminary feature extraction(SPFE)module to better preserve the subtle details that may be overlooked by the original PointNet++model.In addition,a weighted-sum local feature aggregation(WSLFA)mechanism is proposed to replace the max pooling in PointNet++to better perform feature aggregation.The experimental results on 52 testing datasets using the network trained on 160 annotated 3D intra-oral scanner images demonstrate that our improved PointNet++method achieves a segmentation accuracy of 97.68%,and performs well under different dental conditions.展开更多
文摘In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.
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.
基金supported by the 2022 Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund(No.L222110).
文摘Segmentation of a complete set of teeth from three-dimensional(3D)intra-oral scanner images is a crucial step in tooth identification procedures.In large-scale disasters with many victims,teeth are often the preferred and reliable source for victim identification due to their hard and non-deformable characteristics.In this paper we present a study on the automatic segmentation of a complete set of teeth from intra-oral scanner images.We propose a tooth segmentation method based on an improved PointNet++architecture.To address the problem of inadequate segmentation capability of the teeth-gingival boundary of PointNet++,we introduce a single-point preliminary feature extraction(SPFE)module to better preserve the subtle details that may be overlooked by the original PointNet++model.In addition,a weighted-sum local feature aggregation(WSLFA)mechanism is proposed to replace the max pooling in PointNet++to better perform feature aggregation.The experimental results on 52 testing datasets using the network trained on 160 annotated 3D intra-oral scanner images demonstrate that our improved PointNet++method achieves a segmentation accuracy of 97.68%,and performs well under different dental conditions.
