目的·针对错畸形生长发育高峰期骨骼形态变化复杂、难以精准评估的临床难点，利用扩散模型与Transformer深度学习算法构建颈椎图像分割模型并评估其分割性能。方法·使用基于Transformer与扩散模型相结合的方法对185例正畸患者（44例来自重庆医科大学附属口腔医院，141例来自西安交通大学口腔医院）的头颅侧位片进行精准的颈椎分割。首先对图像进行预处理，裁剪出感兴趣的颈椎骨区域，随机将所有数据划分为训练集（79.6%）和测试集（20.4%）。利用U-Net构成的扩散模型和条件模型进行特征提取，引入Transformer模块学习噪声和语义特征之间的相互作用。将多尺度图像进行融合，以增强低对比度图像中的细微结构和边界纹理特征。将该方法与U-Net和SOLOv2方法进行比较，通过Dice相似系数（Dice similarity coefficient，DSC）、交并比（intersection over union，IoU）2项指标定量比较颈椎图像分割性能。通过医师的人工标注结果和模型可视化结果对分割性能进行定性评估。结果·基于Transformer的扩散模型颈椎图像分割方法的DSC和IoU评分分别达到93.3%和87.5%，明显优于U-Net和SOLOv2方法（在DSC上分别领先3.0%和4.1%，在IoU上分别领先5.2%和7.1%）。尽管单张图像的处理时间较长，但分割精度显著提升。相较于U-Net和SOLOv2，基于Transformer的扩散模型颈椎图像分割方法在处理复杂、低对比度和边界模糊的图像时表现出更高的稳定性和鲁棒性，能够精准分割出颈椎骨的清晰边界和完整结构。结论·基于Transformer的扩散模型颈椎图像分割网络能够增强颈椎图像中的边缘和纹理特征，更容易识别不同椎骨的边界，从而获得自动、准确、稳健的颈椎分割结果，可辅助颈椎骨成熟度分析。

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.