NiftyNet Materials

Help

• NiftyNet Documentation
• NiftyNet Mailing List

Youtube

• NiftyNet: Deep Learning platform for medical image analysis - Jorge Cardoso (UCL)
• MED-NIPS 2017 - Jorge Cardoso
• Deep Learning In Practice

Papers

• Gibson and Li et al. 2017
• Li et al. 2017

Demos (from niftynet github)

• https://github.com/NifTK/NiftyNet/tree/dev/demos

Install NiftyNet

For Windows/Linux/Mac WITH GPU (modern Nvidia GPU)

• step 1: install Anaconda(python 3.7,64bit) (or Miniconda)

  Anaconda download

  Note: When installing, check add path!

  

• step 2: run the following command line by line in your terminal

  #create virtual env(A virual env will not mess your installed stuff)
  conda create -n niftynet_env -y python=3.6 conda pip tensorflow-gpu==1.12.0
  
  #enter virtual env. For Linux/Mac use `source activate niftynet_env`
  activate niftynet_env
  conda install -y -c anaconda opencv==3.4.2
  conda install -y -c simpleitk simpleitk==1.2.0
  conda install -y -c anaconda scikit-image==0.14.2
  pip install niftynet==0.5.0
  #leave virtual env. For Linux/Mac, use `source deactivate`
  deactivate

For Windows/Linux/Mac WITHOUT GPU

• step 1: install Anaconda(python 3.7,64bit) (or Miniconda)

  Anaconda download

  Note: When installing, check add path!

  

• step 2: run the following command line by line in your terminal

  #create virtual env(A virual env will not mess your installed stuff)
  conda create -n niftynet_env -y python=3.6 conda pip tensorflow==1.12.0
  
  #enter virtual env. For Linux/Mac use `source activate niftynet_env`
  activate niftynet_env
  conda install -y -c anaconda opencv==3.4.2
  conda install -y -c simpleitk simpleitk==1.2.0
  conda install -y-c anaconda scikit-image==0.14.2
  pip install niftynet==0.5.0
  #leave virtual env. For Linux/Mac: use `source deactivate`
  deactivate

Quick start

#enter virtual env. For Linux/Mac use `source activate niftynet_env`
activate niftynet_env
mkdir test_niftynet
cd test_niftynet
# note: the data and model will be saved to ~/niftynet, not your current directory!
net_download dense_vnet_abdominal_ct_model_zoo
net_segment inference -c ~/niftynet/extensions/dense_vnet_abdominal_ct/config.ini

#The segmentation output of this example application should be located at
~/niftynet/models/dense_vnet_abdominal_ct/segmentation_output/100__niftynet_out.nii.gz

Check segmented results

You can use itk-snap to check the segmented results:

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Appendix

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Run NiftyNet with singularity container(note: Linux with GPU only)

# install singularity first
sudo apt-get install -y singularity-container

# pull sinularity image
singularity pull --name deeplearning_gpu.simg shub://yinglilu/deeplearning_gpu_singularity:1.0.0

# run NiftNet command: singularity exec --nv niftynet_gpu.simg <NiftyNet command> 

# for instance:
singularity exec -e --nv deeplearning_gpu.simg net_download dense_vnet_abdominal_ct_model_zoo

singularity exec -e --nv deeplearning_gpu.simg net_segment inference -c ~/niftynet/extensions/dense_vnet_abdominal_ct/config.ini

# Singularity bind your host $HOME to container's $HOME automatically. 
# The segmentation output of this example application should be located at
~/niftynet/models/dense_vnet_abdominal_ct/segmentation_output/100__niftynet_out.nii.gz

build your own singularity container by following:

• github: deeplearning_gpu_singularity