Image processing tools and ruffus based pipeline for radiomics feature analysis

Just run super-build.sh

• Required
• Script engine and useful modules

http://conda.pydata.org/miniconda.html - !recommend!
https://www.python.org/downloads/

Required modules - pandas, ruffus, SimpleITK, scipy, numpy, ipython, matplotlib install_modules.sh or install_modules.cmd is available to install these modules using conda.

• Recommended
• Image viewer, contour editor, simple image processing tool

http://download.slicer.org/

• gcc or visual studio
• cmake
• ITK

./build.sh

1. DICOMTagReader - Display entire DICOM tags

DICOMTagReader [DICOM directory]

2. 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]

3. DICOM-RT2NRRDConverter - DICOM-RT to nrrd

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

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}]

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

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

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

TBD - modeling code for radiomics features

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)

2. 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'

3. Run radiomics pipeline

$ python script/run_lungx.py or script/run_lungx.py

4. Analysis feature data output files (intermediate images and feature data) will be generated in output

TrainingSet feature_list_TrainingSet.csv TestSet feature_list_TestSet.csv