The classic metaphyseal lesion (CML) is a unique fracture highly specific for infant abuse. This fracture is often subtle in radiographic appearance and commonly occurs in the distal tibia. The development of an automated model that can accurately identify distal tibial radiographs with CMLs is important to assist radiologists in detecting these fractures. However, building such a model typically requires a large and diverse training dataset. To address this problem, we propose a novel diffusion model for data augmentation called masked conditional diffusion model (MaC-DM). In contrast to previous generative models, our approach produces a wide range of realistic-appearing synthetic images of distal tibial radiographs along with their associated segmentation masks. MaC-DM achieves this by incorporating weighted segmentation masks of the distal tibias and CML fracture sites as image conditions for guidance. The augmented images produced by MaC-DM significantly enhance the performance of various commonly used classification models, accurately distinguishing normal distal tibial radiographs from those with CMLs. Additionally, it substantially improves the performance of different segmentation models, accurately labeling areas of the CMLs on distal tibial radiographs. Furthermore, MaC-DM can control the size of the CML fracture in the augmented images.

中文翻译：

---

通过条件扩散模型改进经典干骺端病变的放射线图像分析


典型的干骺端病变（CML）是一种对婴儿虐待具有高度特异性的独特骨折。这种骨折在放射学表现上通常很微妙，并且通常发生在胫骨远端。开发能够准确识别 CML 远端胫骨 X 线照片的自动化模型对于帮助放射科医生检测这些骨折非常重要。然而，构建这样的模型通常需要大量且多样化的训练数据集。为了解决这个问题，我们提出了一种新的数据增强扩散模型，称为掩蔽条件扩散模型（MaC-DM）。与以前的生成模型相比，我们的方法生成了各种逼真的远端胫骨放射线照片及其相关分割掩模的合成图像。 MaC-DM 通过将远端胫骨和 CML 骨折部位的加权分割掩模作为指导图像条件来实现这一目标。 MaC-DM 生成的增强图像显着增强了各种常用分类模型的性能，能够准确地区分正常的远端胫骨 X 光片与 CML 的 X 光片。此外，它还大大提高了不同分割模型的性能，在远端胫骨 X 光片上准确标记 CML 区域。此外，MaC-DM 可以控制增强图像中 CML 骨折的大小。