Data Augmentation in Class-Conditional Diffusion Model for Semi-Supervised Medical Image Segmentation

Accurate segmentation of specific organs or diseased tissues in medical images is crucial for precise diagnosis and effective treatment planning. While fully-supervised deep learning methods have demonstrated remarkable performance, their effectiveness heavily relies on the availability of a substantial number of labeled images. Unfortunately, acquiring and manually labeling a large medical dataset is often expensive and impractical, especially for rare diseases, due to challenges related to data sharing and privacy. To address this limitation, a class-conditional diffusion model is proposed to synthesize realistic medical images, thereby augmenting small-scale datasets with high-quality samples. The diffusion-based method generates an unlimited number of authentic medical images, each conditioned on specific class labels, offering a valuable contribution to dataset augmentation strategies. To evaluate the utility of the generated data, synthetic images are incorporated into the genuine labeled dataset, thereby creating augmented datasets. By utilizing the generated data as unlabeled augmented data for the original dataset, our synthetic data is effectively integrated with semi-supervised medical image segmentation algorithms. This integration successfully combines the diversity of synthetic images with the advantages of semi-supervised learning, resulting in a more comprehensive utilization of limited small-scale data. Our experimental results on two public datasets have demonstrated that our class-conditional diffusion model can generate high-fidelity synthetic medical images. Furthermore, it enables semi-supervised methods to achieve superior segmentation performance in comparison to the methods using only original real datasets.