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Radiomics tools

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

Super build

Just run super-build.sh

./super-build.sh

Install software

Python 3.7

Slicer 4.10

Build

  • gcc or visual studio
  • cmake
  • ITK 4.13.2
./build.sh

Tools

1. DICOMTools

  1. DICOMTagReader - Display entire DICOM tags
DICOMTagReader [DICOM directory]
  1. 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]
  1. DICOM-RT2NRRDConverter - DICOM-RT to nrrd

2. ContourTools

  1. STAPLEComparison - variation comparison on multiple contours
  2. ExtractBoundary
  3. GTVs2ITV
  4. HoleGenerator
  5. ROIGenerator
  6. ROI2BinImage
  7. ROICropImage

3. GrowCutSegmentation

NoduleSegmentation - Segment small nodular objects for solid nodule and GGO

NoduleSegmentation [InputImageFile] [SeedPoint_x] [SeedPoint_y] [SeedPoint_z] \
                   [NoduleSize_long] [NoduleSize_short] [OutputImageFile]  

4. Feature Extraction

FeatureExtraction - Extract image features from the nodule segmentation

FeatureExtraction [InputImage] [LabelImage] [FeatureFile] [Label={1}]

5. Python Tools

  1. metadata.py - for handling metadata in csv or xls
  2. organize_features.py - for collecting feature data into a single csv file

6. MATLAB Tools

  1. NRRD4Matlab - for handing nrrd format in MATLAB
  2. PET2SUV - for converting raw PET image to standardized uptake value(SUV)

7. ETC

  1. RegistrationSITK - simple registration code, required SimpleITK module for python
  2. SlicerPythonExtensions - simple extensions for Slicer
    1. InterpolateROIsEffect.py
    2. LineProfile.py

6. LASSO-SVM

TBD - modeling code for radiomics features

Usage

Radiomics feature extraction pipeline example for LUNGx dataset

  1. 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)

  1. 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'  
  1. Run radiomics pipeline
$ python script/run_lungx.py or script/run_lungx.py
  1. Analysis feature data output files (intermediate images and feature data) will be generated in 'output' directory
  • TrainingSet: feature_list_TrainingSet.csv
  • TestSet: feature_list_TestSet.csv

Wookjin Choi [email protected]