White Matter, Ventricle and Subarachnoid Space Segmentation of CT scans, with Normal Pressure Hydrocephalus Predicton
Code implementing the algorithm described in the paper
Zhang et. al., Fully Automated Volumetric Classification in CT Scans for Diagnosis and Analysis of Normal Pressure Hydrocephalus. https://arxiv.org/abs/1901.09088
To download all prerequisites, in the terminal type
pip install -r requirements.txt
FSL is needed to run this code. Please go to the FSL website at https://fsl.fmrib.ox.ac.uk/fsl/fslwiki to download their software.
Note that there is a current known bug to using FSL which requires the user to manually install libopenblas.
All of the CT scans must be in compressed nifti (.nii.gz) format, in a folder named 'Scans'.
Please note the directory containing the 'Scans' folder and use that as an argument --directory=</path/to/directory> when running the code.
The main function nph_prediction.py takes three arguments from the command line:
directory --directory=</path/to/directory>,
model --model=<model>, where <model> can be rf, linear_svm, or rbf_svm, and
--parallel, which is True if typed and False otherwise.
To run with default settings (recommended), type
python nph_prediction.py
For help on the settings, type
python nph_prediction.py -h
The runtime is approximately 10 minutes per image.
The output will be a .csv file to <folder> with the name of the nifti image file, the white matter, ventricle, and subarachnoid space volumes, and another .csv file with the recommendation of 'normal' or 'possible NPH'.
An example CT scan has been included in the Scans directory.
This is an open source image from http://headctstudy.qure.ai/dataset, accompanying paper at:
Sasank Chilamkurthy et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. DOI:https://doi.org/10.1016/S0140-6736(18)31645-3.
Angela Zhang
Po-yu Kao, CT registration
Austin Mcever, testing
Thanks to the Vision Research Laboratory at the University of California, Santa Barbara; Dr. Ashu Shelat at the Cottage Hospital of Santa Barbara; and Dr. Jeff Chen at the University of California, Irvine for their help and support.