MONAI Label is an intelligent image labeling and learning tool that uses AI assistance to reduce the time and effort of annotating new datasets. By utilizing user interactions, MONAI Label trains an AI model for a specific task and continuously learns and updates that model as it receives additional annotated images.

MONAI Label provides multiple sample applications that include state-of-the-art interactive segmentation approaches like DeepGrow and DeepEdit. These sample applications are ready to use out of the box and let you quickly get started on annotating with minimal effort. Developers can also build their own MONAI Label applications with creative algorithms.


$ pip install monailabel

Focuses on Researchers and Data Scientist

MONAI Core is focused on bringing the state-of-the-art to the broader medical community. You can see some of these features below and start using them today through our tutorials.

Continuous Training Loop

Fine-tune your model while annotating

Quickly start creating an annotation model by utilize the continous training loop with MONAI Label.

Multiple Supported Viewers

3D Slicer, OHIF, DSA, and QuPath

Whether you're annotating Radiology or Pathology images, MONAI Label has viewer integration to get you started quickly.

Interactive and Non-interactive Annotation Methods

Multiple Algorithms to Choose

Utilize interactive methods to get started quickly or choose an existing model that already works great.

Which Use Case are you working on?

MONAI Label has integration for both Radiology and Pathology Images. By enabling a workflow that integrates directly into a Radiologist or Pathologist viewer and allowing for continuous learning, MONAI aims to accelerate the adoption of Deep Learning in Medical Imaging.


If you're working in Radiology, start using OHIF or 3D Slicer, which have extensions built to integrate with MONAI Label. You can bring your DICOM or Nifti images into the viewer and start annotating today with models like DeepEdit.


If you're working in Pathology, we've integrated with Digital Slide Archive (DSA), QuPath, and CVAT viewers. MONAI Label is creating a starting point for Pathologists and Data scientists to work together and utilize the benefits of Deep Learning.


If you're working in Endoscopy, we've integrated with CVAT viewer and enabled a fully automated active learning workflow to train a model. MONAI Label enables users to use interactive, segmentation, and classification models over 2D images.

Get started with only a few commands

MONAI Label makes it easy to get started. By following the commands on the right, you'll:

  • Install MONAI Label
  • Download a Sample Radiology App
  • Download a Sample Dataset
  • Start the MONAI Label Server

Designed with Active Learning for Continuous Training

To demonstrate the power of MONAI Label and the Continous Training Loop plus Active Learning, we'll walk through how you'll utilize these techniques as you begin to annotate your data.

Get Involved in the Community

Below are ways that you can start using, contributing, and interacting with other members of the MONAI Label community!


Start contributing to the MONAI Label repo by submiting a bug, requesting a feature, or starting a discussion today!

Sample Applications

We have sample applications for various domains to help you quickly get started. You can also use these sample applications as the basis for your own MONAI Label Applications.


Having trouble? Trying to find out where to contribute? Wnat to share your cool project? Join our MONAI Slack and chat with the community.


Ready to get started using MONAI Core? You can find all of our documentation to help get you started or answer any of your questions!