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monai

Description

monai website

The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.

Environment Modules

Run module spider monai to find out what environment modules are available for this application.

Environment Variables

  • HPC_MONAI_DIR - installation directory
  • HPC_MONAI_BIN - bin directory
  • HPC_MONAI_LIB - lib directory
  • HPC_MONAI_INC - include directory

Additional Usage Information

Training Materials

MONAI on HiPerGator Tutorial Recordings and Slides

Recording: MONAI Label for Medical Imaging

Sldes: MONAICore_tutorial_UF_July_2022.pdf

Recording: MONAI Core

GitHub repository with code examples

MONAI 3-day Bootcamp Recordings and Learning Materials

MONAI Deploy App SDK

Spleen Segmentation Overview Spleen Segmentation Deepdive MedNIST Classification

MONAI Packages

If you would like to install MONAI on your own platforms, here are some useful links:

MONAI Core tutorials

MONAI Usage Examples

MONAI Label

Start the server as a slurm job:

  • Load environment modules 

    ml purge
ml ngc-monailabel/<version>

  • Copy the examle job script to your work directory: /apps/nvidia/containers/monai/start_monai_server_readonly.sh

  • Copy the sample data to your work directory: 

    cp -r /apps/nvidia/containers/monai/apps/deepedit <my_place>
cp -r /apps/nvidia/containers/monai/datasets/Task09_Spleen <my_place>

  • Modify the start_monai_server_readonly.sh line to read: 

    apptainer exec -B /apps/nvidia/containers/monai /apps/nvidia/containers/monai/monailabel/ monailabel start_server --app <my_place>/deepedit --studies <my_place>/Task09_Spleen/imagesTr
  • Start server as a batch job: 
      sbatch start_monai_server_readonly.sh
    Note the server address from job output to use in the next step.

3DSlicer client

  • Start an Open On Demand (OOD) session
  • Start Console in hwgui with 1 GPU: gpu:geforce:1

  • In the console: load & start Slicer 

     ml qt/5.15.4 slicer/4.13.0
 vglrun -d :0.$CUDA_VISIBLE_DEVICES Slicer
    In Slicer GUI:

  • Select module: Active Learning -> MONAILabel

  • Fill in server address, e.g.: http://c1007a-s17:8000/
  • Click on refresh button next to server address
  • Load the next sample

Categories

healthcare