MAISI: Medical AI for Synthetic Imaging

TL;DR

MAISI introduces a diffusion model-based method to generate high-resolution, anatomically accurate synthetic 3D CT images with detailed organ segmentation, addressing data scarcity and privacy issues in medical imaging.

Contribution

The paper presents MAISI, a novel diffusion model approach that produces high-quality synthetic CT images with detailed annotations, enhancing data availability for medical imaging tasks.

Findings

Generated images are realistic and anatomically accurate.

MAISI effectively incorporates organ segmentation for detailed annotations.

Synthetic images improve data diversity for downstream applications.

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.