Guo Dongqian, Han Wencheng, Lyu Pang, Zhou Yuxi, Shen Jianbing
IEEE Trans Med Imaging. 2025 Jul;44(7):2784-2794. doi: 10.1109/TMI.2025.3557430.
Cephalometric landmark detection is essential for orthodontic diagnostics and treatment planning. Nevertheless, the scarcity of samples in data collection and the extensive effort required for manual annotation have significantly impeded the availability of diverse datasets. This limitation has restricted the effectiveness of deep learning-based detection methods, particularly those based on large-scale vision models. To address these challenges, we have developed an innovative data generation method capable of producing diverse cephalometric X-ray images along with corresponding annotations without human intervention. To achieve this, our approach initiates by constructing new cephalometric landmark annotations using anatomical priors. Then, we employ a diffusion-based generator to create realistic X-ray images that correspond closely with these annotations. To achieve precise control in producing samples with different attributes, we introduce a novel prompt cephalometric X-ray image dataset. This dataset includes real cephalometric X-ray images and detailed medical text prompts describing the images. By leveraging these detailed prompts, our method improves the generation process to control different styles and attributes. Facilitated by the large, diverse generated data, we introduce large-scale vision detection models into the cephalometric landmark detection task to improve accuracy. Experimental results demonstrate that training with the generated data substantially enhances the performance. Compared to methods without using the generated data, our approach improves the Success Detection Rate (SDR) by 6.5%, attaining a notable 82.2%. All code and data are available at: https://um-lab.github.io/cepha-generation/.
头影测量标志点检测对于正畸诊断和治疗计划至关重要。然而，数据收集样本的稀缺以及手动标注所需的大量工作严重阻碍了多样化数据集的可用性。这一限制制约了基于深度学习的检测方法的有效性，尤其是那些基于大规模视觉模型的方法。为应对这些挑战，我们开发了一种创新的数据生成方法，能够在无需人工干预的情况下生成多样化的头影测量X线图像以及相应的标注。为此，我们的方法首先利用解剖学先验构建新的头影测量标志点标注。然后，我们使用基于扩散的生成器来创建与这些标注紧密对应的逼真X线图像。为了在生成具有不同属性的样本时实现精确控制，我们引入了一个新颖的提示头影测量X线图像数据集。该数据集包括真实的头影测量X线图像和描述这些图像的详细医学文本提示。通过利用这些详细提示，我们的方法改进了生成过程以控制不同的风格和属性。在大量多样的生成数据的推动下，我们将大规模视觉检测模型引入头影测量标志点检测任务以提高准确性。实验结果表明，使用生成数据进行训练可显著提高性能。与未使用生成数据的方法相比，我们的方法将成功检测率（SDR）提高了6.5%，达到了显著的82.2%。所有代码和数据可在以下网址获取：https://um-lab.github.io/cepha-generation/ 。
