Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation
This work addresses the problem of reducing manual annotation needs for retinal OCT images in biomedical imaging, representing an incremental advance in applying generative models to medical data synthesis.
The paper tackles the challenge of limited annotated data in biomedical image analysis by using denoising diffusion probabilistic models (DDPMs) to synthesize realistic retinal OCT images from rough layer sketches, resulting in improved layer segmentation accuracy and achieving comparable results to models trained on real images.
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.