GliomaGen: Conditional Diffusion for Post-Treatment Glioma MRI Generation

GliomaGen is a generative diffusion model tailored for synthesizing post-treatment glioma MRI images based on anatomical masks. It leverages a modified Med-DDPM architecture to create high-fidelity MRI images conditioned on segmented anatomical features.

Model Overview

GliomaGen aims to address data scarcity in post-treatment glioma segmentation tasks by expanding existing datasets with synthetic, high-quality MRI volumes. The model takes anatomical masks as input and generates multi-modal MRI scans conditioned on segmentation labels.

Model Performance

Quantitative Metrics

Modality	FID (↓)	KID (↓)	MS-SSIM (↑)
t1c	55.20 ± 3.74	0.0293 ± 0.0019	0.7647 ± 0.2106
t2w	54.99 ± 3.23	0.0291 ± 0.0010	0.6513 ± 0.2881
t1n	58.46 ± 3.86	0.0305 ± 0.0011	0.7005 ± 0.2585
t2f	70.42 ± 4.17	0.0370 ± 0.0018	0.7842 ± 0.1551

Usage

To use GliomaGen for MRI generation, see the GitHub repository.

BraTS 2024 Adult Post-Treatment Glioma-Synthetic

Alongisde GliomaGen, a synthetic dataset of $N=2124$ MR images is released on HuggingFace.