simpleDicomViewer 0.0.3

A package to simplify visualizing DICOM data via Jupyter/Python.

Summary

This is a very simplistic DICOM viewer for images and related segmentations (RTSTRUCT and SEG). It was developed as a quick and dirty solution for performing spot checks on data downloaded from The Cancer Imaging Archive using tcia_utils. It was later separated into a stand-alone PyPI package as many users of tcia_utils are not concerned with interactively viewing images and this capability introduced a lot of additional dependencies. There are many other more advanced viewers out there (e.g. 3D Slicer or itkWidgets) that you should try if your data fails with this tool.

Installation

We're installing a forked version of pydicom-seg because the PYPI package is using a very outdated version of jsonschema, which creates a lot of dependency conflicts even though the newer version appears to work without any issues.

# install forked pydicom-seg with updated jsonschema version
!pip install --upgrade -q git+https://github.com/kirbyju/pydicom-seg.git@master

# install simpleDicomViewer
!{sys.executable} -m pip install --upgrade -q simpleDicomViewer```

# Usage

Import using:

`from simpleDicomViewer import dicomViewer`

Examples for using it can be found in [demo.ipynb](https://github.com/kirbyju/simpleDicomViewer/blob/main/demo.ipynb).

# Acknowledgements

A big thanks to [Adam Li](https://github.com/adamli98) who introduced the functionality to display the segmentation overlays.

### Citations:
This repository includes sample data from The Cancer Imaging Archive in the "data" folder which you can use for testing its features.  

1. Zhao, B., Schwartz, L. H., Kris, M. G., & Riely, G. J. (2015). Coffee-break lung CT collection with scan images reconstructed at multiple imaging parameters (Version 3) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2015.u1x8a5nr
2. Wee, L., Aerts, H., Kalendralis, P., & Dekker, A. (2020). RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2020.jit9grk8