Deep-Learning-Based Automatic Measurement of the Distance Between the Maxillary Sinus and Maxillary Posterior Teeth on CBCT Images.
Authors
Affiliations (6)
Affiliations (6)
- Department of Cariology and Endodontology, Peking University School and Hospital of Stomatology, Beijing, China.
- National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials, Beijing, China.
- Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, Beijing, China.
- School of Computer Science, Peking University, Beijing, China.
- School of Software and Microelectronics, Peking University & National Engineering Research Center for Software Engineering, Peking University & Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, China.
- Department of Oral Emergency, Peking University School and Hospital of Stomatology, Beijing, China.
Abstract
To explore a deep learning (DL) model for determining the relationship between the maxillary sinus (MS) and maxillary posterior teeth (MPT) based on cone beam computed tomography (CBCT) images and measuring the distance automatically between the MS and MPT using a 3D point cloud algorithm. A CBCT dataset containing 88 maxillary sinuses (MSs) and 352 maxillary posterior teeth (MPT) was annotated, and the MS-MPT distances were measured by clinicians as the ground truth. A segmentation model for MSs and MPT in CBCT images based on the U-Net convolutional block attention (CBAM) architecture was trained and assessed using a 3-fold cross-validation strategy. Then, calibrated point clouds were reconstructed using segmented anatomical structure data, and the Euclidean distances between the MS and MPT were measured; the minimum distance was identified as the MS-MPT distance. The performance of the model in terms of segmentation and distance measurement was evaluated, and the results were compared with the ground truth. Our segmentation model achieved a mean Dice similarity coefficient (DSC) of 0.959 and a mean Jaccard coefficient of 0.922 for MSs and a mean DSC of 0.913 and a mean Jaccard coefficient of 0.851 for MPT. The MS-MPT distances determined by clinicians and the 3D point cloud method demonstrated strong consistency (ϒ > 0.993, p < 0.01). In terms of the model and clinicians, the mean negative signed error was 0.63 mm (95% CI, 0.59-0.66 mm), and the successful detection rate (SDR) for the root apex of MPT reached 70.3% at the 1 mm threshold. In this study, an automated framework that combines deep learning-driven segmentation and three-dimensional point cloud analysis was developed to quantify the relationship between the maxillary sinus and maxillary posterior teeth and achieved reliable detection accuracy across diverse anatomical variations in CBCT scans.