Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation

IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1–5

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 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.

Authors 8

1. Yuli Wu Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Imaging and Computer Vision,Aachen,Germany

2. Weidong He Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Imaging and Computer Vision,Aachen,Germany

3. Dennis Eschweiler Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Imaging and Computer Vision,Aachen,Germany

4. Ningxin Dou
   Affiliation as printed

   Shenzhen Eye Hospital,Shenzhen,China

5. Zixin Fan
   Affiliation as printed

   Shenzhen Eye Hospital,Shenzhen,China

6. Shengli Mi

   Tsinghua Shenzhen International Graduate School · Tsinghua University

   Affiliation as printed

   Tsinghua University,Bio-Manufacturing Engineering Laboratory of Tsinghua SIGS,Shenzhen,China

7. Peter Walter Aachen Department of Ophthalmology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Department of Ophthalmology,Aachen,Germany

8. Johannes Stegmaier Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Imaging and Computer Vision,Aachen,Germany