ellisdg/3DUnetCNN

Health v2 · maintenance only

Adoption not part of the score

2235 stars · 664 forks observed · 2026-10-13

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

A PyTorch library for building, training, and applying 3D U-Net convolutional neural networks for medical image segmentation. It provides configuration-driven training pipelines and tutorials based on MICCAI challenge datasets like BraTS.

Use cases

• segment brain tumors from MRI scans
• train a 3D U-Net on volumetric medical images
• apply deep learning to medical imaging segmentation tasks
• reproduce MICCAI challenge segmentation results
• segment 3D anatomical structures in NIfTI volumes

When to choose

• you need 3D (volumetric) image segmentation with U-Net architectures in PyTorch
• you are working with medical imaging data like MRI or CT scans
• you want a configuration-driven training setup with worked examples

When to avoid

• you need 2D image segmentation only
• you want a general-purpose computer vision toolkit outside medical imaging
• you need a maintained GUI or clinical application rather than a research library

Facets

library · maturity active

deep-learning machine-learning image-processing computer-vision deep-learning healthcare machine-learning image-processing python cross-platform unet medical-imaging segmentation pytorch 3d-convolution mri brats

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ellisdg/3DUnetCNN")