Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis.
Journal
IEEE journal of biomedical and health informatics
Series/Report No.
IEEE Journal of Biomedical and Health Informatics
Journal Volume
28
Journal Issue
7
Start Page
4084-4093
ISSN
2168-2208
Date Issued
2024-07
Author(s)
DOI
10.1109/JBHI.2024.3385504
Abstract
Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 dice score of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of the proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues.
Subjects
anonymization
Conditional diffusion models
data augmentation
generative models
semantic image synthesis
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
journal article