MONAI Label · Annotate

Interactive annotation
for medical imaging

A server-side annotation engine with active learning: it suggests the next volume to label, refines its predictions from your corrections, and plugs straight into 3D Slicer, OHIF, and MITK. DICOM and NIfTI, in and out.

Get Started Documentation GitHub

start an annotation server

$ pip install monailabel

$ monailabel apps --download \
    --name radiology --output apps

$ monailabel start_server \
    --app apps/radiology \
    --studies datasets/imagesTr \
    --conf models deepedit

Capabilities

AI assistance inside the viewers you already use

MONAI Label runs as a server behind your viewer. Annotators keep their tools; the model keeps learning from every correction.

AI-Assisted Annotation

Interactive models (DeepEdit and DeepGrow) turn a few clicks into full 3D segmentations

Predictions refine in real time as you correct them

Fine-tunes UNet, UNETR, and SwinUNETR backbones in the background

Clinical Integration

Native plugins for 3D Slicer, OHIF, and MITK, plus QuPath, DSA, and CVAT

Reads studies directly from DICOMweb / PACS or a local folder

Multi-user annotation against one shared server

Extensible Platform

Apps are plain Python: swap in your own models and inference logic

Pluggable active-learning strategies (random, epistemic, custom)

REST API for integrating with existing labeling pipelines

Use Cases

Ready-made apps for three imaging domains

Each sample app ships with pre-configured models and viewer integrations. Start annotating in your domain without writing code.

Radiology

Organ segmentation, tumor delineation, and anatomical measurement on CT and MRI, through 3D Slicer, OHIF, or MITK, with DeepEdit and Segmentation models pre-wired.

Radiology sample app

Pathology

Cell detection and tissue classification on whole-slide images, with QuPath, Digital Slide Archive, and CellProfiler integrations for microscopy-scale data.

Pathology sample app

Endoscopy

Polyp detection and surgical-tool tracking in video, with CVAT integration for efficient frame-by-frame annotation and automated propagation.

Endoscopy sample app

Quick Start

From zero to an annotation server in four commands

Install the package, grab the radiology app and a sample dataset, and start the server. Then connect from 3D Slicer or OHIF and begin annotating with AI assistance.

Install MONAI Label

One pip install gets you the server, CLI, and sample-app tooling.

$ pip install monailabel

Download the radiology app

Pre-configured with DeepEdit and segmentation models for CT/MRI.

$ monailabel apps --download \
    --name radiology \
    --output apps

Download a sample dataset

The Medical Segmentation Decathlon spleen task is a good first dataset.

$ monailabel datasets --download \
    --name Task09_Spleen \
    --output datasets

Launch the server

Point your viewer at the server URL and start annotating.

$ monailabel start_server \
    --app apps/radiology \
    --studies datasets/Task09_Spleen/imagesTr \
    --conf models deepedit

Full quickstart guide, including viewer setup

Active Learning

Every correction makes the next label cheaper

The server scores unlabeled volumes by model uncertainty and serves the most informative one next, so annotation effort goes where it teaches the model most.

50–80%

Less annotation time

Faster model convergence

90%

Accuracy with fewer labels

Smart Sample Selection

Uncertainty sampling surfaces the hardest cases first
Diversity metrics keep training data varied
Ensemble disagreement flags ambiguous anatomy

Continuous Model Improvement

Models retrain on new labels while you keep annotating
Transfer learning from pre-trained MONAI bundles
UNet, UNETR, and SwinUNETR supported out of the box

Quality Assurance

Live validation metrics during annotation sessions
Per-prediction uncertainty estimates
Automated label quality checks

Learning

Resources & Training

Videos, guides, and example apps to go from first install to a custom annotation workflow.

Video Tutorials

MONAI Label Deep Dive Series

Comprehensive tutorials covering all aspects of MONAI Label

MONAI Bootcamp

Overview and hands-on training from our latest bootcamp

Documentation & Guides

Quickstart Guide

Get up and running with MONAI Label in minutes

API Documentation

Detailed technical documentation and API references

Sample Applications

Radiology, pathology, and endoscopy apps with best practices

Reference

How to cite MONAI Label

If MONAI Label supports your research, please cite the Medical Image Analysis paper:

@article{monailabel2024,
    title={MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images},
    author={Diaz-Pinto, Andres and Alle, Sachidanand and Nath, Vishwesh and Tang, Yucheng and Ihsani, Alvin and Asad, Muhammad and P{\'e}rez-Garc{\'i}a, Fernando and Mehta, Pritesh and Li, Wenqi and Flores, Mona and Roth, Holger R. and Vercauteren, Tom and Xu, Daguang and Dogra, Prerna and Ourselin, Sebastien and Feng, Andrew and Cardoso, M. Jorge},
    journal={Medical Image Analysis},
    year={2024},
    doi={10.1016/j.media.2024.103207}
}

View on DOI

Connect

Shape AI-assisted annotation

Get help, share your annotation workflows, and join the Human-AI Interaction Working Group: the community standardizing how humans and models annotate together.

Documentation

From basic concepts to custom apps and advanced annotation strategies.

Read the Docs

Human-AI Interaction WG

The working group defining standard interfaces for AI-assisted annotation cycles.

Join the Group

GitHub Repository

Source code, sample apps, and discussions for real-world annotation scenarios.

View Code

Slack Community

Real-time help and collaboration with the MONAI Label community.

Join Slack

Interactive annotation for medical imaging