Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929303344644096 |
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| author | Mei, Siyuan Fan, Fuxin Wagner, Fabian Thies, Mareike Gu, Mingxuan Sun, Yipeng Maier, Andreas |
| author_facet | Mei, Siyuan Fan, Fuxin Wagner, Fabian Thies, Mareike Gu, Mingxuan Sun, Yipeng Maier, Andreas |
| contents | Deep learning-based medical image processing algorithms require representative data during development. In particular, surgical data might be difficult to obtain, and high-quality public datasets are limited. To overcome this limitation and augment datasets, a widely adopted solution is the generation of synthetic images. In this work, we employ conditional diffusion models to generate knee radiographs from contour and bone segmentations. Remarkably, two distinct strategies are presented by incorporating the segmentation as a condition into the sampling and training process, namely, conditional sampling and conditional training. The results demonstrate that both methods can generate realistic images while adhering to the conditioning segmentation. The conditional training method outperforms the conditional sampling method and the conventional U-Net. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03541 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models Mei, Siyuan Fan, Fuxin Wagner, Fabian Thies, Mareike Gu, Mingxuan Sun, Yipeng Maier, Andreas Image and Video Processing Computer Vision and Pattern Recognition Deep learning-based medical image processing algorithms require representative data during development. In particular, surgical data might be difficult to obtain, and high-quality public datasets are limited. To overcome this limitation and augment datasets, a widely adopted solution is the generation of synthetic images. In this work, we employ conditional diffusion models to generate knee radiographs from contour and bone segmentations. Remarkably, two distinct strategies are presented by incorporating the segmentation as a condition into the sampling and training process, namely, conditional sampling and conditional training. The results demonstrate that both methods can generate realistic images while adhering to the conditioning segmentation. The conditional training method outperforms the conditional sampling method and the conventional U-Net. |
| title | Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.03541 |