MAISI: Medical AI for Synthetic Imaging
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918246953779200 |
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| author | Guo, Pengfei Zhao, Can Yang, Dong Xu, Ziyue Nath, Vishwesh Tang, Yucheng Simon, Benjamin Belue, Mason Harmon, Stephanie Turkbey, Baris Xu, Daguang |
| author_facet | Guo, Pengfei Zhao, Can Yang, Dong Xu, Ziyue Nath, Vishwesh Tang, Yucheng Simon, Benjamin Belue, Mason Harmon, Stephanie Turkbey, Baris Xu, Daguang |
| contents | Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet, MAISI can process organ segmentation, including 127 anatomical structures, as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic, anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11169 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAISI: Medical AI for Synthetic Imaging Guo, Pengfei Zhao, Can Yang, Dong Xu, Ziyue Nath, Vishwesh Tang, Yucheng Simon, Benjamin Belue, Mason Harmon, Stephanie Turkbey, Baris Xu, Daguang Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet, MAISI can process organ segmentation, including 127 anatomical structures, as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic, anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data. |
| title | MAISI: Medical AI for Synthetic Imaging |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.11169 |