This is the code for the paper
Workflow-centred open-source fully automated lung volumetry in chest CT
F. Jungmann*, S. Brodehl*, R. Buhl, P. Mildenberger, E. Schömer, C. Düber, D. Pinto dos Santos
(* equal contribution)
Clinical Radiology, Volume 75, Issue 1, 2020, Pages 78.e1-78.e7
Our open source algorithm allows fast and fully automated calculation of lung volume in multidetector computed tomography. The paper shows that lung volume measured by CT correlated significantly with pulmonary function testing. The integration of the algorithm into the clinical workflow offers measurements at the start of the reporting process without manual interaction.
We provide:
- Code to run the algorithm on CT images
- Code to produce reports from CT images
If you find this work useful in your research, please cite:
@article{JUNGMANN202078.e1,
title = "Workflow-centred open-source fully automated lung volumetry in chest CT",
author = "F. Jungmann and S. Brodehl and R. Buhl and P. Mildenberger and E. Schömer and C. Düber and D. Pinto dos Santos"
journal = "Clinical Radiology",
volume = "75",
number = "1",
pages = "78.e1 - 78.e7",
year = "2020",
issn = "0009-9260",
doi = "https://doi.org/10.1016/j.crad.2019.08.010",
url = "http://www.sciencedirect.com/science/article/pii/S0009926019305197",
}
The current version requires in particular the following libraries / versions. See requirements.txt for the full list of requirements.
- Python 3, version
2.xmight work, no guarantees, no support. - pydicom 1.0.0 or newer.
- OpenCV 3 python bindings or newer, version
2.xmight work.
The easiest way to install those dependencies is by using the requirements.txt file with pip3.
pip3 install -r requirements.txt
- Guido Zuidhof's Full Preprocessing Tutorial for Data Science Bowl 2017 for inspiration on data preprocessing (with Python).
- @rhaxton for inspiration on in-memory data decompression paired with pydicom.