# MONAI

> Open-source PyTorch-based framework for deep learning in healthcare imaging, with medical-specific transforms, networks, losses, and metrics.

MONAI (Medical Open Network for AI) is an open-source, PyTorch-based framework for deep learning in healthcare imaging, developed by Project MONAI. The project says it was started by NVIDIA and King's College London and is now a community of 30+ institutions. The current release listed is 1.6.1, and the code is licensed under Apache 2.0.

## What It Is

MONAI Core is a training and research library for medical imaging AI. It provides flexible pre-processing for multi-dimensional medical imaging data, plus domain-specific networks, losses and evaluation metrics. It is built on PyTorch, so standard PyTorch components such as DataLoader work with it, and it supports multi-GPU, multi-node data parallelism. It is installed with pip, conda or a Docker image.

## Domain tooling and models

The project site describes DICOM and NIfTI I/O, spatial transforms for 3D medical volumes, and segmentation metrics such as Dice, Hausdorff and Surface Distance. It lists reference implementations including MAISI, UNETR, SwinUNETR, VISTA-3D and Auto3DSeg. MONAI Bundles package weights, training configs, metadata and inference code, and the same format is used by the Model Zoo for sharing community models.

## The wider MONAI ecosystem

Core is one of three projects that share a bundle format. MONAI Label is an active-learning annotation server that plugs into 3D Slicer, OHIF and MITK. MONAI Deploy covers the path to clinical pipelines, with DICOM and FHIR I/O and containerized MAP packaging. Tutorials, Colab notebooks such as the MedNIST demo, and API documentation on Read the Docs support onboarding.

## Features
- Flexible pre-processing for multi-dimensional medical imaging data
- Domain-specific networks, losses and evaluation metrics
- DICOM and NIfTI I/O
- Spatial transforms for 3D medical volumes
- Segmentation metrics (Dice, Hausdorff, Surface Distance)
- MONAI Bundle format for packaging weights, configs and inference code
- Model Zoo of community models
- Reference architectures including MAISI, UNETR, SwinUNETR, VISTA-3D and Auto3DSeg
- Multi-GPU multi-node data parallelism
- Docker image and Colab tutorials

## Integrations
PyTorch, 3D Slicer, OHIF, MITK, Docker, Kubernetes, DICOM, FHIR

## Platforms
WINDOWS, MACOS, LINUX, API, DEVELOPER_SDK, CLI

## Pricing
Open Source

## Version
1.6.1

## Links
- Website: https://project-monai.github.io/
- Documentation: https://monai.readthedocs.io/en/stable/
- Repository: https://github.com/Project-MONAI/MONAI
- EveryDev.ai: https://www.everydev.ai/tools/monai