Improving the radiographic image analysis of the classic metaphyseal lesion via conditional diffusion models.

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.