Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation

arXiv:2311.05479v2 Announce Type: replace-cross
Abstract: 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 …

abstract analysis annotated data arxiv biomedical challenge cs.cv data deep generative models deep learning denoising diffusion eess.iv generative generative models image images issue layer modern physics.med-ph regard segmentation synthesis type