Deep learning techniques for anatomical landmark localization (ALL) have shown great success, but their reliance on large annotated datasets remains a problem due to the tedious and costly nature of medical data acquisition and annotation. While traditional data augmentation, variational autoencoders (VAEs), and generative adversarial networks (GANs) have already been used to synthetically expand medical datasets, diffusion-based generative models have recently started to gain attention for their ability to generate high-quality synthetic images. In this study, we explore the use of denoising diffusion probabilistic models (DDPMs) for generating medical images and their corresponding heatmaps of landmarks to enhance the training of a supervised deep learning model for ALL. Our novel approach involves a DDPM with a 2-channel input, incorporating both the original medical image and its heatmap of annotated landmarks. We also propose a novel way to assess the quality of the generated images using a Markov Random Field (MRF) model for landmark matching and a Statistical Shape Model (SSM) to check landmark plausibility, before we evaluate the DDPM-augmented dataset in the context of an ALL task involving hand X-Rays.

本研究解决了解剖标志定位（ALL）中对大量标注数据集依赖的问题，通过使用去噪扩散概率模型（DDPM）生成高质量合成医学图像和相应的标志热图，从而增强监督深度学习模型的训练。研究结果表明，该方法在改进手部X光片的ALL任务中具有显著效果，展示了其在医学影像数据增强中的潜在应用价值。

基于DDPM的解剖标志定位的合成增强