Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis
CoRR(2023)
摘要
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
accuracy in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the
0.6531 accuracy of real images, and outperforms baseline models. Combined with
real images, it further increases segmentation accuracy to 0.6675, showing the
potential of our 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. We provide the code and model weights for Med-DDPM on our
GitHub repository (https://github.com/mobaidoctor/med-ddpm/) to support
reproducibility.
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