Creating a Segmentation App

This tutorial shows how to create an organ segmentation application for a PyTorch model that has been trained with MONAI. Please note that the example code used in the Jupyter Notebook is based on an earlier version of the segmentation application, i.e., not using MONAI Bundle Inference Operator, and the code is not necessarily the same as the latest source code on Github.

Setup

# Create a virtual environment with Python 3.7.
# Skip if you are already in a virtual environment.
# (JupyterLab dropped its support for Python 3.6 since 2021-12-23.
#  See https://github.com/jupyterlab/jupyterlab/pull/11740)
conda create -n monai python=3.7 pytorch torchvision jupyterlab cudatoolkit=11.1 -c pytorch -c conda-forge
conda activate monai

# Launch JupyterLab if you want to work on Jupyter Notebook
jupyter-lab

Executing from Jupyter Notebook

• Creating a Segmentation App with MONAI Deploy App SDK
  • Creating Operators and connecting them in Application class
    • Setup environment
    • Download/Extract ai_spleen_bundle_data from Google Drive
    • Setup imports
    • Creating Model Specific Inference Operator classes
      • SpleenSegOperator
    • Creating Application class
  • Executing app locally
    • spleen_seg_operator.py
    • app.py
    • __main__.py
  • Packaging app
  • Executing packaged app locally

Download 03_segmentation_app.ipynb

Executing from Shell

Please note that this part of the example uses the latest application source code on Github, as well as the corresponding test data.

# Clone the github project (the latest version of main branch only)
git clone --branch main --depth 1 https://github.com/Project-MONAI/monai-deploy-app-sdk.git

cd monai-deploy-app-sdk

# Install monai-deploy-app-sdk package
pip install --upgrade monai-deploy-app-sdk

# Download/Extract ai_spleen_bundle_data zip file from https://drive.google.com/file/d/1cJq0iQh_yzYIxVElSlVa141aEmHZADJh/view?usp=sharing

# Download the zip file containing both the model and test data
pip install gdown
gdown https://drive.google.com/uc?id=1Uds8mEvdGNYUuvFpTtCQ8gNU97bAPCaQ

# After downloading it using gdown, unzip the zip file saved by gdown
unzip -o ai_spleen_seg_bundle_data.zip

# Install necessary packages from the app; note that numpy-stl and trimesh are only
# needed if the application uses the STL Conversion Operator
pip install monai pydicom highdicom SimpleITK Pillow nibabel scikit-image numpy-stl trimesh

# Local execution of the app
python examples/apps/ai_spleen_seg_app/app.py -i dcm/ -o output -m model.ts

# Package app (creating MAP docker image) using `-l DEBUG` option to see progress.
# This assumes that nvidia docker is installed in the local machine.
# Please see https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker to install nvidia-docker2.
monai-deploy package examples/apps/ai_spleen_seg_app --tag seg_app:latest --model model.ts -l DEBUG

# Run the app with docker image and input file locally
monai-deploy run seg_app:latest dcm/ output

Creating a Deploy App with MONAI Deploy App SDK and MONAI Bundle Creating a Segmentation App with MONAI Deploy App SDK