slicerutil 0.1.0

A utility package for implementing functions in 3D Slicer

Slicer_scripting_tutorial

A repository built for the slicer tutorial run by Marcus Milantoni and Edward Wang.

Table of Contents

• License
• Requirements
• Setup
• Tutorial
• Example patients
• Acknowledgements

License

The following repository is under MIT license. For more information, visit LICENSE.

Requirements

• numpy
• matplotlib

The full list can be found in requirements.txt

Setup

Downloading the 3D slicer application & supporting software

Please follow the steps provided bellow:

1. Visit slicer to download the application.
2. Visit anaconda to download python3/ jupyter notebook.
3. Visit Visual Studio Code to download the source-code editor (optional).
4. From the Extensions Manager widget, download the SlicerJupyter, MeshToLabelMap, PETDICOMExtension (if working with PET DICOMS), SlicerRT (if working with radiotherapy data).
   • The Slicer application needs to restart to install the extensions.

Set up the SlicerJupyter

1. Using the search widget in Slicer, open the SlicerJupyter extension by searching for JupyterKernel.

   

2. Click the "Start Jupyter Server" button. A JupyterLab notebook will open when the setup is complete.

3. Click the "Jupyter server in external Python environment" and copy the command to clipboard.

4. Open the anaconda prompt (Terminal if on mac) and paste the command.

5. (Optional) open an external environment (Visual Studio Code) and select the Slicer kernel!

Install python packages

1. Open the Python console by clicking the python logo on the widget bar.
2. Import pip with

   import pip
   

3. install the packages with

   pip.main(['install', 'requirements.txt'])
   

   • Find the requirements in the requirements.txt file.

Tutorial

cervical_cancer_tutorial

This tutorial was created to demonstrate the basic_functions module on the cervical cancer example patient. This tutorial runs through most of the functions that are used in Slicer Python image processing.

Examples

A list of all the the example patients provided.

Brain Pre-op

This dataset is provided by the Brain Resection Multimodal Imaging Database (ReMIND) on the Cancer Imaging Archive. The data includes a patient that was surgically treated with image-guided tumor resection between 2018 and 2022. The preoperative T1 & T2 MRIs are used as well as the segmentation of the tumor. More information can be found here.

Juvekar, P., Dorent, R., Kögl, F., Torio, E., Barr, C., Rigolo, L., Galvin, C., Jowkar, N., Kazi, A., Haouchine, N., Cheema, H., Navab, N., Pieper, S., Wells, W. M., Bi, W. L., Golby, A., Frisken, S., & Kapur, T. (2023). The Brain Resection Multimodal Imaging Database (ReMIND) (Version 1) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/3RAG-D070

Cervical Cancer

This dataset is provided by the CC-TUMOR-HETEROGENITY collection on the Cancer Imaging Archive. The dataset includes a PET/CT DICOM of a patient with cervical cancer, and structure RTstructs created using TotalSegmentator. More information can be found here.

Mayr, N., Yuh, W. T. C., Bowen, S., Harkenrider, M., Knopp, M. V., Lee, E. Y.-P., Leung, E., Lo, S. S., Small Jr., W., & Wolfson, A. H. (2023). Cervical Cancer – Tumor Heterogeneity: Serial Functional and Molecular Imaging Across the Radiation Therapy Course in Advanced Cervical Cancer (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/ERZ5-QZ59

Acknowledgements

The slicer scripting tutorial would not be possible without the following open source software:

• Slicer
• Numpy
• Matplotlib