  Modern biomedical image analysis using deep learning often encounters the
challenge of limited annotated data. To overcome this issue, deep generative
models can be employed to synthesize realistic biomedical images. In this
regard, we propose an image synthesis method that utilizes denoising diffusion
probabilistic models (DDPMs) to automatically generate retinal optical
coherence tomography (OCT) images. By providing rough layer sketches, the
trained DDPMs can generate realistic circumpapillary OCT images. We further
find that more accurate pseudo labels can be obtained through knowledge
adaptation, which greatly benefits the segmentation task. Through this, we
observe a consistent improvement in layer segmentation accuracy, which is
validated using various neural networks. Furthermore, we have discovered that a
layer segmentation model trained solely with synthesized images can achieve
comparable results to a model trained exclusively with real images. These
findings demonstrate the promising potential of DDPMs in reducing the need for
manual annotations of retinal OCT images.
