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--- |
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language: en |
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tags: |
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- diffusion-models |
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- medical-imaging |
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- glioma |
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- synthetic-data |
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- MRI |
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license: mit |
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datasets: |
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- BraTS2024 |
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model-index: |
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- name: GliomaGen |
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results: |
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- task: |
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type: image-generation |
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dataset: |
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name: BraTS2024 Adult Post-Treatment Glioma |
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type: medical-imaging |
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metrics: |
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- name: FID (t1c) |
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type: frechet-inception-distance |
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value: 55.2028 ± 3.7446 |
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- name: FID (t2w) |
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type: frechet-inception-distance |
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value: 54.9974 ± 3.2271 |
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- name: KID (t1c) |
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type: kernel-inception-distance |
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value: 0.0293 ± 0.0019 |
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- name: MS-SSIM (t1c) |
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type: multi-scale-structural-similarity |
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value: 0.7647 ± 0.2106 |
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--- |
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# GliomaGen: Conditional Diffusion for Post-Treatment Glioma MRI Generation |
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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. |
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## Model Overview |
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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. |
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## Model Performance |
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### **Quantitative Metrics** |
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| Modality | FID (↓) | KID (↓) | MS-SSIM (↑) | |
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|----------|--------|--------|-------------| |
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| t1c | 55.20 ± 3.74 | 0.0293 ± 0.0019 | 0.7647 ± 0.2106 | |
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| t2w | 54.99 ± 3.23 | 0.0291 ± 0.0010 | 0.6513 ± 0.2881 | |
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| t1n | 58.46 ± 3.86 | 0.0305 ± 0.0011 | 0.7005 ± 0.2585 | |
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| t2f | 70.42 ± 4.17 | 0.0370 ± 0.0018 | 0.7842 ± 0.1551 | |
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## Usage |
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To use GliomaGen for MRI generation, see the [GitHub repository](https://github.com/elijahrenner/gliomagen). |
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## BraTS 2024 Adult Post-Treatment Glioma-Synthetic |
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Alongisde GliomaGen, a synthetic dataset of $N=2124$ MR images is released on HuggingFace. |
