  The classic metaphyseal lesion (CML) is a distinct injury that is highly
specific for infant abuse. It commonly occurs in the distal tibia. To aid
radiologists detect these subtle fractures, we need to develop a model that can
flag abnormal distal tibial radiographs (i.e. those with CMLs). Unfortunately,
the development of such a model requires a large and diverse training database,
which is often not available. To address this limitation, we propose a novel
generative model for data augmentation. Unlike previous models that fail to
generate data that span the diverse radiographic appearance of the distal
tibial CML, our proposed masked conditional diffusion model (MaC-DM) not only
generates realistic-appearing and wide-ranging synthetic images of the distal
tibial radiographs with and without CMLs, it also generates their associated
segmentation labels. To achieve these tasks, MaC-DM combines the weighted
segmentation masks of the tibias and the CML fracture sites as additional
conditions for classifier guidance. The augmented images from our model
improved the performances of ResNet-34 in classifying normal radiographs and
those with CMLs. Further, the augmented images and their associated
segmentation masks enhanced the performance of the U-Net in labeling areas of
the CMLs on distal tibial radiographs.
