ImprintLab/MedSegDiff

Health v2 · maintenance only

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Adoption not part of the score

1363 stars · 202 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MedSegDiff is a diffusion probabilistic model framework for segmenting and reconstructing organs and tissues from medical images, with a transformer-based V2 variant. It provides training and sampling scripts for datasets like BraTS and ISIC.

Use cases

• segment organs from MRI or CT scans
• apply diffusion models to medical image segmentation
• train a segmentation model on BraTS brain tumor data
• reconstruct tissue masks from medical images
• compare transformer-based diffusion segmentation models
• run fast diffusion sampling with DPM-Solver

When to choose

• you need state-of-the-art diffusion-based medical image segmentation
• you want a research-backed model with published papers (MIDL 2023, AAAI 2024)
• you work with 2D or 3D medical imaging datasets like BraTS or ISIC

When to avoid

• you need a production-ready clinical imaging tool with a GUI
• you want lightweight real-time segmentation on CPU
• you need non-medical general image segmentation out of the box

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision deep-learning artificial-intelligence image-processing python diffusion-models medical-imaging image-segmentation denoising-diffusion transformer research-code gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ImprintLab/MedSegDiff")