Objective ·To construct a cervical vertebra image segmentation model by using a diffusion model with the Transformer deep learning algorithm, and evaluate its segmentation performance, to address the clinical challenge of accurately assessing complex changes in skeletal morphology during the growth and developmental peaks of malocclusion. Methods ·Accurate cervical vertebra segmentation was performed on cephalometric radiographs from 185 orthodontic patients (44 cases from the Stomatological Hospital of Chongqing Medical University and 141 cases from the Stomatological Hospital of Xi'an Jiaotong University) by using a method combining Transformer and diffusion models. First, the images were preprocessed to crop out the cervical vertebra region of interest, and all data were randomly divided into a training set (79.6%) and a test set (20.4%). The diffusion model and a conditional model based on U-Net were utilized for feature extraction, with a Transformer module introduced to learn the interaction between noise and semantic features. Multi-scale images were fused to enhance fine structure and boundary texture features in low-contrast images. The proposed method was compared with U-Net and SOLOv2 methods. The segmentation performance was quantitatively evaluated by two metrics, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), and also qualitatively assessed through physicians' manual annotations and model visualization results. Results ·The cervical vertebra segmentation method based on Transformer and diffusion models achieved DSC and IoU scores of 93.3% and 87.5%, respectively, significantly outperforming the U-Net and SOLOv2 methods (with improvements of 3.0% and 4.1% in DSC, and 5.2% and 7.1% in loU, respectively). Despite the longer processing time for a single image, segmentation accuracy was significantly improved. Compared with U-Net and SOLOv2, the proposed method also showed higher stability and robustness in processing complex, low-contrast and blurred-boundary images, and was able to accurately segment the cervical vertebrae with clear boundaries and complete structures. Conclusion ·The Transformer-based diffusion model for cervical vertebra segmentation can enhance the edge and texture features in cervical vertebra images and recognize the boundaries of different vertebrae more easily. Thus, automatic, accurate, and robust cervical vertebra segmentation results are achieved, which can assist in cervical vertebral maturation analysis.