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.

翻译：现代生物医学图像分析利用深度学习时常面临标注数据匮乏的挑战。为解决该问题，深度生成模型可被用于合成逼真的生物医学图像。为此，我们提出一种利用去噪扩散概率模型（DDPMs）自动生成视网膜光学相干断层扫描（OCT）图像的合成方法。通过提供粗略的层结构草图，训练后的DDPMs能够生成逼真的视盘周围OCT图像。我们进一步发现，通过知识适应可获得更精确的伪标签，这对分割任务大有裨益。借此，我们观察到层分割精度的一致提升，该结论通过多种神经网络得到验证。此外，我们发现仅使用合成图像训练的层分割模型，其性能可与完全依赖真实图像训练的模型相媲美。这些发现表明DDPMs在减少视网膜OCT图像人工标注需求方面具有显著潜力。