MONAI

Health v2 · maintenance only

Adoption not part of the score

8788 stars · 1671 forks observed · 2026-10-19

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

MONAI is a PyTorch-based open-source framework for deep learning in healthcare imaging, providing domain-specific transforms, 3D architectures, losses, and metrics. It is part of the PyTorch Ecosystem and includes companion projects for active-learning annotation (MONAI Label) and clinical deployment (MONAI Deploy).

Use cases

• train a 3D segmentation model on CT or MRI volumes
• preprocess DICOM and NIfTI medical images for deep learning
• evaluate segmentation with Dice and Hausdorff metrics
• deploy a trained model as a clinical inference pipeline
• use pre-trained models like UNETR or SwinUNETR from the Model Zoo
• run multi-GPU distributed training on medical imaging data
• generate synthetic CT images with MAISI

When to choose

• you are doing deep learning research or production work on medical imaging
• you need 3D spatial transforms and domain-specific architectures on top of PyTorch
• you want reproducible, portable model packaging via MONAI Bundles
• you need clinically validated segmentation metrics and losses

When to avoid

• your project is general-purpose computer vision on natural images
• you need a non-PyTorch framework like TensorFlow or JAX
• you only need lightweight image viewing or DICOM parsing without deep learning

Facets

framework · maturity stable

deep-learning machine-learning image-processing data-science healthcare deep-learning machine-learning image-processing artificial-intelligence python cross-platform medical-imaging pytorch segmentation dicom nifti healthcare-imaging model-zoo gpu docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Project-MONAI/MONAI")