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README.md

Spleen Segmentation with Liver Example using NVIDIA Models and MONAI

We have put together a training example that segments the Spleen in 3D CT Images. At the end is an example of combining both the Spleen model and the Liver model.

Nvidia has changed some of the models used in this tutorial and it may crash, if you have issues, try commenting out the liver model, we are working on a patch

Introduction

Two pre-trained models from NVIDIA are used in this training, a Spleen model and Liver. The Spleen model is additionally retrained on the medical decathlon spleen dataset: http://medicaldecathlon.com/ Data is not necessary to be downloaded to run the notebook. The notebook downloads the data during it's run. The notebook uses the Python package MONAI, the Medical Open Network for Artificial Intelligence.

Outcomes

After following along with this notebook the user will be familiar with:

  • Downloading public datasets using MONAI
  • Using MONAI transformations for training
  • Downloading a pretrained NVIDIA Clara model using MONAI
  • Retrain model using MONAI
  • Visualizing medical images in python/matplotlib

Installing MONAI

Please follow the instructions on MONAI's website for up to date install. Installing MONAI in a notebook environment can be completed with the commands:

  • !python -c "import monai" || pip install -q 'monai[all]'
  • !python -c "import matplotlib" || pip install -q matplotlib

Dependencies

It is recommended to use an NVIDIA GPU for training. If the user does not have access to a NVIDIA GPU then it is recommended to skip the training cells.

The following packages and versions were installed during the testing of this notebook:

  • MONAI version: 0.8.1
  • Numpy version: 1.21.1
  • Pytorch version: 1.9.0
  • Pytorch Ignite version: 0.4.8
  • Nibabel version: 3.2.1
  • scikit-image version: 0.18.2
  • Pillow version: 8.3.1
  • Tensorboard version: 2.5.0
  • gdown version: 3.13.0
  • TorchVision version: 0.10.0+cu111
  • tqdm version: 4.61.2
  • lmdb version: 1.2.1
  • psutil version: 5.8.0
  • pandas version: 1.3.0
  • einops version: 0.3.0
  • transformers version: 4.18.0
  • mlflow version: 1.25.1