  Dataset expansion can effectively alleviate the problem of data scarcity for
medical image segmentation, due to privacy concerns and labeling difficulties.
However, existing expansion algorithms still face great challenges due to their
inability of guaranteeing the diversity of synthesized images with paired
segmentation masks. In recent years, Diffusion Probabilistic Models (DPMs) have
shown powerful image synthesis performance, even better than Generative
Adversarial Networks. Based on this insight, we propose an approach called
DiffuseExpand for expanding datasets for 2D medical image segmentation using
DPM, which first samples a variety of masks from Gaussian noise to ensure the
diversity, and then synthesizes images to ensure the alignment of images and
masks. After that, DiffuseExpand chooses high-quality samples to further
enhance the effectiveness of data expansion. Our comparison and ablation
experiments on COVID-19 and CGMH Pelvis datasets demonstrate the effectiveness
of DiffuseExpand. Our code is released at
https://github.com/shaoshitong/DiffuseExpand.
