Contents

Installation

An overview of pyKNEEr, its installation, and its demo are presented in a youtube video, which we recommend watching as a comprehensive introduction. The current and following pages provide more details.

Note

The commands in this documentation are for Mac OS. If you work on Windows:

  • Substitute / with \

  • Use the python terminal provided in the Anaconda distribution

python

We recommend to install python through Anaconda, a platform providing a complete distribution

  • Download the latest release of Anaconda for your operating system

  • Install Anaconda as you would do for any software (see the official documentation)

Important

Make sure that you are using python 3

pyKNEEr

Go to terminal, copy/paste the following line and press enter:

  pip install pykneer

The installation contains elastix v4.8 for atlas-based segmentation. If you work on a Windows or a Linux computer, you might need to set the environment variables for elastix

Note

We recommend to install pyKNEEr in a python virtual environment, although it is not necessary

Demo

To become familiar with pyKNEEr, we provide a demo that you can replicate following the step-by-step instructions in the following pages

  • Download the latest version of the demo images here (2.1 GB)

    Important

    Make sure that all the folder names that constitute the path to the demo images have no spaces.
    E.g. /home/learning_pykneer/demo/ and not /home/learning pykneer/demo/

  • Unzip the file and open it. It contains two folders and some files:

    • input: It is the basic folder to work with pyKNEEr:

      • The folder original contains images of subjects 01, which contains:

        • DESS images to get familiar with atlas-based segmentation and \(T_2\) mapping

        • cubeQuant images to get familiar with multimodal segmentation and \(T_{1 \rho}\) mapping

        Both acquisitions will be used to get familiar with preprocessing and morphology analysis

      • The folder reference contains the folder newsubject, which contains:

        • reference.mha: Reference image for the atlas-based segmentation

        • reference_f.mha: Femur mask of the reference image

        • reference_fc.mha: Femoral cartilage mask of the reference image

        Note

        You can use this reference image and its masks also when segmenting your own data

      • Input files (.txt) to run pyKNEEr workflow and a subset of Jupyter notebooks (.ipynb). These files are explained in the next pages

      Important

      In the following instructions we will assume that input is our working directory

    • output: It contains the outputs of the demo, so you can compare your findings with ours

Introduction Preprocessing