--- language: en tags: - diffusion-models - medical-imaging - glioma - synthetic-data - MRI license: mit datasets: - BraTS2024 model-index: - name: GliomaGen results: - task: type: image-generation dataset: name: BraTS2024 Adult Post-Treatment Glioma type: medical-imaging metrics: - name: FID (t1c) type: frechet-inception-distance value: 55.2028 ± 3.7446 - name: FID (t2w) type: frechet-inception-distance value: 54.9974 ± 3.2271 - name: KID (t1c) type: kernel-inception-distance value: 0.0293 ± 0.0019 - name: MS-SSIM (t1c) type: multi-scale-structural-similarity value: 0.7647 ± 0.2106 --- # 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](https://github.com/elijahrenner/gliomagen). ## BraTS 2024 Adult Post-Treatment Glioma-Synthetic Alongisde GliomaGen, a synthetic dataset of $N=2124$ MR images is released on HuggingFace.