Fully automatic coronary calcium risk assessment using Deep Learning. This work was published in nature communications - Open access!
If you use code or parts of this code in your work, please cite our publication:
Zeleznik, R., Foldyna, B., Eslami, P. et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun 12, 715 (2021). https://doi.org/10.1038/s41467-021-20966-2
The DeepCAC repository is structured as follows:
- All the source code to run the deep-learning-based fully automatic coronary calcium risk assessment pipeline is found under the
srcfolder. - Four sample subjects' CT data and the associated manual segmentation masks (heart + CAC), as well as all the models weights necessary to run the pipeline, are stored under the
datafolder. - AI and manual results for the NLST cohort in the paper as well as the statistical analysis are located in the
statsfolder.
Additional details on the content of the subdirectories and their structure can be found in the markdown files stored in the former.
This code was developed and tested using Python 2.7.17.
For the code to run as intended, all the packages under requirements.txt should be installed. In order not to break previous installations and ensure full compatibility, it's highly recommended to create a virtual environment to run the DeepCAC pipeline in. Here follows an example of set-up using python virtualenv:
# install python's virtualenv
sudo pip install virtualenv
# parse the path to the python2 interpreter
export PY2PATH=$(which python2)
# create a virtualenv with such python2 interpreter named "venv"
# (common name, already found in .gitignore)
virtualenv -p $PY2PATH venv
# activate the virtualenv
source venv/bin/activate
At this point, (venv) should be displayed at the start of each bash line. Furthermore, the command which python2 should return a path similar to /path/to/folder/venv/bin/python2. Once the virtual environment is activated:
# once the virtualenv is activated, install the dependencies
pip install -r requirements.txt
At this stage, everything should be ready for the data to be processed by the DeepCAC pipeline. Additional details can be found in the markdown file under src.
The virtual environment can be deactivated by running:
deactivate
Code development: RZ
Code testing, refactoring and documentation: DB
The code and data of this repository are provided to promote reproducible research. They are not intended for clinical care or commercial use.
The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.