--- title: "MONAI Label" description: "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 learning from user interactions, it refines its predictions as you annotate." canonical: https://project-monai.github.io/label.html audience: [engineer] last_updated: 2026-06-11 source: label.html --- 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](#quick-start) [Documentation](https://monai.readthedocs.io/projects/label/en/latest/) [GitHub](https://github.com/Project-MONAI/MONAILabel) 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 Use Case](/assets/img/figures/label-use-case-radiology.png) ### 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](https://github.com/Project-MONAI/MONAILabel/tree/main/sample-apps/radiology) ![Pathology Use Case](/assets/img/figures/label-use-case-pathology.png) ### 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](https://github.com/Project-MONAI/MONAILabel/tree/main/sample-apps/pathology) ![Endoscopy Use Case](/assets/img/figures/label-use-case-endoscopy.png) ### Endoscopy Polyp detection and surgical-tool tracking in video, with CVAT integration for efficient frame-by-frame annotation and automated propagation. [Endoscopy sample app](https://github.com/Project-MONAI/MONAILabel/tree/main/sample-apps/endoscopy) 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. 1 ### Install MONAI Label One pip install gets you the server, CLI, and sample-app tooling. $ pip install monailabel 2 ### Download the radiology app Pre-configured with DeepEdit and segmentation models for CT/MRI. ``` $ monailabel apps --download \ --name radiology \ --output apps ``` 3 ### Download a sample dataset The Medical Segmentation Decathlon spleen task is a good first dataset. ``` $ monailabel datasets --download \ --name Task09_Spleen \ --output datasets ``` 4 ### 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](https://monai.readthedocs.io/projects/label/en/latest/quickstart.html) 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. ![Active Learning Framework Diagram](/assets/img/figures/label-active-learning-framework.png) 50–80% Less annotation time 2x 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 ](https://www.youtube.com/playlist?list=PLtoSVSQ2XzyD4lc-lAacFBzOdv5Ou-9IA)[ #### MONAI Bootcamp Overview and hands-on training from our latest bootcamp ](https://www.youtube.com/watch?v=-HAryYAO5J4) ### Documentation & Guides [ #### Quickstart Guide Get up and running with MONAI Label in minutes ](https://monai.readthedocs.io/projects/label/en/latest/quickstart.html)[ #### API Documentation Detailed technical documentation and API references ](https://monai.readthedocs.io/projects/label/en/latest/index.html)[ #### Sample Applications Radiology, pathology, and endoscopy apps with best practices ](https://github.com/Project-MONAI/MONAILabel/tree/main/sample-apps) 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](https://doi.org/10.1016/j.media.2024.103207) 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](https://monai.readthedocs.io/projects/label/en/latest/index.html) ### Human-AI Interaction WG The working group defining standard interfaces for AI-assisted annotation cycles. [Join the Group](wg_human_ai_interaction.html) ### GitHub Repository Source code, sample apps, and discussions for real-world annotation scenarios. [View Code](https://github.com/Project-MONAI/MONAILabel) ### Slack Community Real-time help and collaboration with the MONAI Label community. [Join Slack](https://join.slack.com/t/projectmonai/shared_invite/zt-3hucgm02q-i8Bn9XofDZs2UGOH4jUl4w)