NifTK/NiftyNet

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

1374 stars · 401 forks observed · 2026-08-28

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

NiftyNet is a TensorFlow-based open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy. It provides modular network components, pre-trained models, and support for 2D/2.5D/3D/4D medical imaging inputs, but is no longer maintained.

Use cases

• segment 3D medical images like MRI and CT scans with deep learning
• train a 3D U-net or V-net on my own medical imaging dataset
• use pre-trained networks for medical image segmentation
• build GANs or autoencoders for medical image synthesis
• run multi-GPU training on volumetric medical images
• evaluate medical image segmentation with standard metrics

When to choose

• you need a legacy TensorFlow 1.x platform for medical image segmentation research
• you want to reproduce results from papers that used NiftyNet
• you need pre-trained 3D medical imaging networks like HighRes3DNet or DeepMedic

When to avoid

• you are starting a new project - use MONAI instead, which is its successor
• you need actively maintained software or modern TensorFlow/PyTorch support
• you need clinically validated software - NiftyNet is explicitly not for clinical use

Facets

library · maturity abandoned

deep-learning machine-learning image-processing gpu-computing deep-learning machine-learning healthcare image-processing python windows medical-imaging segmentation tensorflow cnn gan autoencoder unmaintained image-guided-therapy 3d-images linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="NifTK/NiftyNet")