MAISI: Medical AI for Synthetic Imaging
This addresses data scarcity and privacy concerns in medical imaging for healthcare applications, though it appears incremental as it builds on existing diffusion models.
The paper tackles challenges in medical imaging like data scarcity and privacy by introducing MAISI, a diffusion model that generates synthetic 3D CT images up to 512 x 512 x 768 resolution with 127 anatomical structures, showing promising potential for mitigating these issues.
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.