/OncoGEN

Generalisation tool for magnetic resonance images

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

OncoGEN

Project Status: Active – The project has reached a stable, usable state and is being actively developed.

OncoGEN is a generalisation tool designed to standardise medical images of oncological diseases, enabling advanced image processing techniques. In general, the workflow consist of:

  1. Transformation of DICOM image series to NIFTI with dicom2niix [1].
  2. Bias correction of the image intensities with the N4 algorithm [2].
  3. Co-registration into an atlas with CaPTk [3].
  4. Skull stripping with BrainMaGe [4].

Integration of OncoGEN

OncoGEN is part of a module based umbrella software project for numerical simulations of patient-specific cancer diseases, see following figure. From given input states of medical images the disease is modelled and its evolution is simulated giving possible predictions. In this way, a digital cancer patient is created, which could be used as a basis for further research, as a decision-making tool for doctors in diagnosis and treatment and as an additional illustrative demonstrator for enabling patients understand their individual disease. All parts resolve to an open-access framework, that is ment to be an accelerator for the digital cancer patient. Each module can be installed and run independently. The current state of development comprises the following modules

workflow.png

Software availability

You can either follow the installation instruction below or use the already pre-installed virtual boxes via the following Links:

Installation and Machine Requirements

There are two different options the installation can be done. First, is the stand-alone installation, where OncoGEN is simply installed in an Anaconda environment. The other way is to install OncoFEM (https://github.com/masud-src/OncoFEM) first and add the missing packages. This installation was tested on

  • a virtual box created with a linux mint 21.2 cinnamon, 64 bit system and 8 GB RAM on a local machine (intel cpu i7-9700k with 3.6 GHz, 128 GB RAM).
  • a MacBook Pro (Mac14,7) with a Apple M2 Chip (8 cores) and 24 GB memory. The integrated GPU got 10 cores.

Download the repository.

git clone https://github.com/masud-src/OncoGEN/
cd OncoGEN
chmod +x install_prerequisites.sh
./install_prerequisites.sh

If not installed already, here is a script to install anaconda on your system. Please restart the terminal.

chmod +x install_anaconda.sh
./install_anaconda.sh

Next the dependency BrainMaGe [4] is downloaded and the user can decide in which way the environment should be set up. The user can choose between

  1. Set up according to BrainMaGe
  2. Set up according to BrainMaGe without fixed package versions
  3. Set up in stand-alone according to OncoFEM legacy (recommended for Stand-alone installation)
  4. Append the OncoFEM environment

Please restart the terminal afterwards.

chmod +x create_environment.sh
./create_environment.sh

To install the dependencies execute the following shell script. Feel free to change the activated environment in the first line. Please restart the terminal afterwards.

conda activate oncogen
chmod +x install_BrainMaGe.sh
./install_BrainMaGe.sh

Now, CaPTk can be installed, the SRI24 atlas can be downloaded and the OncoGEN package can be installed with

chmod +x install_captk.sh
./install_captk.sh
chmod +x get_sri24.sh
./get_sri24.sh
chmod +x install_oncogen.sh
./install_oncogen.sh

For quick evaluation of the software is correctly installed, please open python in a terminal and run

import oncogen

Tutorial

There is a tutorial for the umbrella software project provided on DaRUS (https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-4639). You can download via the web interface or by running the following line into your desired directory.

curl --output tutorial https:/darus.uni-stuttgart.de/api/access/dataset/:persistentId/?persistentId=doi:10.18419/darus-4639

The tutorial can be started with

conda activate oncogen
python oncogen_tut_01_separated_full.py

How to develop

You can modify the existing algorithms, respectively expand the existing by your own. Therefore, you can fork and ask for pull requests.

How to cite

If you are using using OncoGEN please also cite the related publication

Onco: An umbrella Python framework for modelling and simulation of oncological scenarios*. Marlon Suditsch, Arndt Wagner, Tim Ricken, Journal of Computational Science (2025). 10.1016/j.jocs.2025.102533

@article{SUDITSCH2025102533,
title = {Onco*: An umbrella Python framework for modelling and simulation of oncological scenarios},
journal = {Journal of Computational Science},
pages = {102533},
year = {2025},
issn = {1877-7503},
doi = {https://doi.org/10.1016/j.jocs.2025.102533},
url = {https://www.sciencedirect.com/science/article/pii/S1877750325000109},
author = {Marlon Suditsch and Arndt Wagner and Tim Ricken},
}

Literature

1 Li X, Morgan PS, Ashburner J, Smith J, Rorden C (2016) The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods. 264:47-56. doi: 10.1016/j.jneumeth.2016.03.001. PMID: 26945974

2 Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010 Jun;29(6):1310-20. doi: 10.1109/TMI.2010.2046908. Epub 2010 Apr 8. PMID: 20378467; PMCID: PMC3071855.

3 C.Davatzikos, S.Rathore, S.Bakas, S.Pati, M.Bergman, R.Kalarot, P.Sridharan, A.Gastounioti, N.Jahani, E.Cohen, H.Akbari, B.Tunc, J.Doshi, D.Parker, M.Hsieh, A.Sotiras, H.Li, Y.Ou, R.K.Doot, M.Bilello, Y.Fan, R.T.Shinohara, P.Yushkevich, R.Verma, D.Kontos, "Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome", J Med Imaging, 5(1):011018, 2018, DOI:10.1117/1.JMI.5.1.011018

4 Thakur S, Doshi J, Pati S, Rathore S, Sako C, Bilello M, Ha SM, Shukla G, Flanders A, Kotrotsou A, Milchenko M, Liem S, Alexander GS, Lombardo J, Palmer JD, LaMontagne P, Nazeri A, Talbar S, Kulkarni U, Marcus D, Colen R, Davatzikos C, Erus G, Bakas S. Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. Neuroimage. 2020 Oct 15;220:117081. doi: 10.1016/j.neuroimage.2020.117081. Epub 2020 Jun 27. PMID: 32603860; PMCID: PMC7597856.