Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866915808215564288 |
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| author | Wu, Yuli He, Weidong Eschweiler, Dennis Dou, Ningxin Fan, Zixin Mi, Shengli Walter, Peter Stegmaier, Johannes |
| author_facet | Wu, Yuli He, Weidong Eschweiler, Dennis Dou, Ningxin Fan, Zixin Mi, Shengli Walter, Peter Stegmaier, Johannes |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_05479 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation Wu, Yuli He, Weidong Eschweiler, Dennis Dou, Ningxin Fan, Zixin Mi, Shengli Walter, Peter Stegmaier, Johannes Image and Video Processing Computer Vision and Pattern Recognition Medical Physics 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. |
| title | Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2311.05479 |