What’s New


  • MONAI Core dependency updated to >= 0.13.0

  • Bug Fixes and Documentation updates


  • Multi Users authentication and KeyCloak Integration

    • MONAI Label APIs with Keycloak integration for user authentication and role based access.

    • Login support for 3D Slicer via MONAILabel + KeyClock.

  • Whole Body CT segmentation

  • MONAI Bundle Support Improvements

    • Support visualization of bundle config option.

    • Enhancement of monai-zoo access.

    • Support bundle downloading from NGC.

    • Enhacement of multi-gpu training of bundles.

  • CI/CD and tests

    • blossom CI/CD and pre-merge pipeline enabled.

    • Increased Unit tests coverage to 80%.

  • Updated pretrained models:

    • segmentation

    • deepedit

  • New MONAI Label Tutorial Series

    • Quickstart tutorials and installation instructions in notebooks.

  • Documentation enhancements

    • New look in main README, Sample App READMEs, and Plugin READMEs.


  • Pathology Improvements

  • QuPath Improvements

    • User experience enhancements

    • MONAI Label specific Toolbar actions

    • Drag and Drop ROI to run auto-segmentation models

    • Single click to run interaction models (NuClick)

    • Support Next Sample/ROI for Active Learning

  • Experiment Management

  • 3D Slicer: Detection model support in MONAI Bundle App for Radiology use-case

  • Multi-GPU/Multi-Threaded support for Batch Inference



  • Endoscopy Sample App

    • Tool Tracking segmentation model

    • InBody vs OutBody classification model

    • DeepEdit interaction model for annotating tool

    • CVAT Integration to support automated workflow to run Active Learning Iterations

  • Improving performance for Radiology App

    • Support cache for pre-transforms in case repeated inference for interaction models

    • Support cache for DICOM Web API responses

    • Fix DICOM Proxy for wado/qido

  • Multi Stage vertebra segmentation

  • Improvements for Epistemic based active learning strategy

  • Support for MONAI 1.0.0


  • MONAI Bundle App - Pull compatible bundles from MONAI Zoo

    • spleen_ct_segmentation

    • spleen_deepedit_annotation

    • others

  • Support for MONAI 0.9.1


  • Fix MONAI dependency version to 0.9.0


  • Pathology Sample App

    • DeepEdit, Segmentation, NuClick models

    • Digital Slide Archive plugin

    • QuPath plugin

  • Histogram-based GraphCut and Gaussian Mixture Model (GMM) based methods for scribbles

  • Support for MONAI (supports 0.9.0 and above)

  • Radiology Sample App (Aggregation of previous radiology models) - DeepEdit, Deepgrow, Segmentation, SegmentationSpleen models

  • NrrdWriter for multi-channel arrays

  • 3D Slicer Fixes

    • Support Segmentation Editor and other UI enhancements

    • Improvements for Scribble Interactions

    • Support for .seg.nrrd segmentation files

    • Support to pre-load existing label masks during image fetch/load

  • Static checks using pre-commit ci


  • Multi GPU support for training

    • Support for both Windows and Ubuntu

    • Option to customize GPU selection

  • Multi Label support for DeepEdit

    • DynUNET and UNETR

  • Multi Label support for Deepgrow App

    • Annotate multiple organs (spleen, liver, pancreas, unknown etc..)

    • Train Deepgrow 2D/3D models to learn on existing + new labels submitted

  • 3D Slicer plugin

    • Multi Label Interaction

    • UI Enhancements

    • Train/Update specific model

  • Performance Improvements

    • Dataset (Cached, Persistence, SmartCache)

    • ThreadDataloader

    • Early Stopping

  • Strategy Improvements to support Multi User environment

  • Extensibility for Server APIs


  • Support for DICOMWeb connectivity to PACS

  • Annotations support via OHIF UI enabled in MONAI Label Server

  • Support for native and custom scoring methods to support next image selection strategies

    • Native support for scoring and image selection using Epistemic Uncertainty and Test-time Augmentations (Aleatoric Uncertainty)

  • Scribbles-based annotation support for all sample apps

  • Simplified sample apps with default behavior for generic annotation tasks