MAISI: Medical AI for Synthetic Imaging

Pengfei Guo, Can Zhao, Dong Yang, Ziyue Xu, Vishwesh Nath, Yucheng Tang, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, Daguang Xu; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4430-4441

Abstract

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.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Guo_2025_WACV, author = {Guo, Pengfei and Zhao, Can and Yang, Dong and Xu, Ziyue and Nath, Vishwesh and Tang, Yucheng and Simon, Benjamin and Belue, Mason and Harmon, Stephanie and Turkbey, Baris and Xu, Daguang}, title = {MAISI: Medical AI for Synthetic Imaging}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {4430-4441} }