Note: Here is a full system for lung cancer screening radiomics. https://github.com/taznux/LungCancerScreeningRadiomics
Image processing tools and ruffus based pipeline for radiomics feature analysis
Just run super-build.sh
./super-build.sh
./build.sh
DICOMTagReader - Display entire DICOM tags
DICOMTagReader [DICOM directory]
DICOM2NRRDConverter - DICOM to nrrd (Slicer file format)
Simple recursive converting for single patient data
DICOM2NRRDConverter [DICOM directory] [nrrd directory]
For large data
python DICOM2NRRDConverter.py [DICOM directory] [nrrd directory]
DICOM-RT2NRRDConverter - DICOM-RT to nrrd
NoduleSegmentation - Segment small nodular objects for solid nodule and GGO
NoduleSegmentation [InputImageFile] [SeedPoint_x] [SeedPoint_y] [SeedPoint_z] \
[NoduleSize_long] [NoduleSize_short] [OutputImageFile]
FeatureExtraction - Extract image features from the nodule segmentation
FeatureExtraction [InputImage] [LabelImage] [FeatureFile] [Label={1}]
TBD - modeling code for radiomics features
Radiomics feature extraction pipeline example for LUNGx dataset
Download DICOM images
https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM+Lung+CT+Challenge
Download all DICOM images to 'DATA'
You can use the included metadata files for LUNGx (TrainingSet.csv and TestSet.csv)
Environmental parameters
Set your parameters in script/run_lungx.py (recommend default setting).
experiment_set = 'TrainingSet'
# experiment_set = 'TestSet'
output_path = 'output'
data_path = 'DATA'
dicom_path = data_path + '/DOI'
image_path = data_path + '/' + experiment_set
nodule_info_path = './' + experiment_set + '.csv'
Run radiomics pipeline
$ python script/run_lungx.py or script/run_lungx.py
Analysis feature data output files (intermediate images and feature data) will be generated in 'output' directory
Wookjin Choi wchoi1022@gmail.com