Public and private CT scan dataset DICOM Preprocessing Scripts

Warning: Depecrated version for academic usage (Apr 2019), Unstable

DICOM Preprocessing script for DICOM conversion of public and private CT scan datasets Some scripts are adapted from LIDC-IDRI-preprocessing

Requirements:

1. OS: Windows
2. Standard python libraries (glob, os, subprocess, xml), jupyter notebook, and scikit packages (numpy, pandas, scikit package, and xml)
3. SimpleITK Library

See more information related to DICOM Preprocessing:

1. MITK phenotyping
2. 3D slicer

For public datasets

• Download data from the LIDC-IDRI website --> Download DICOM files and XML annotations using the "NBIA Data Retrriever" and choose "Classic Directory Name" option instead of "Descriptive Directory Name".
• Change the paths in "lidc_data_to_nifti.py"
  • path_to_executables : Path where the command line tool from MITK Phenotyping can be found
  • path_to_dicoms : Folder which contains the DICOM image files (not the segmentation dicoms)
  • path_to_xmls : Folder that contains the XML which describes the nodules Following output paths needs to be defined:
  • path_to_nrrds : Folder that will contain the created Nrrd / Nifti Files
  • path_to_planars :Folder that will contain the Planar figure for each subject
  • path_to_characteristics : Path to a CSV File, where the characteristic of a nodule will be stored. If the file exists, the new content will be appended.
  • path_to_error_file : Path to an error file where error messages are written to. Existing files will be appended.
• Run the script "lidc_data_to_nifti.py"

For private datasets

• Run the folder in numbered sequence and adapt it to your private datasets annotation
  1. Match_CSV annotation
  2. Read_Metadata_from_DICOM
  3. Convert_XML
  4. DICOM_to_NRRD
• If you have some problem related to automatic script preprocessing, please use 3rd party apps mentioned above (MITK Phenotyping/3D Slicer) to manually convert your datasets to NRRD files

Output files on Subfolders:

1. NRRD-Files (Whole DICOM series)
2. Planar Figures (pixel-wise masking of nodules in single plane)
3. Nifti file/nii.gz (segmentation of nodule)

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http://www.apache.org/licenses/LICENSE-2.0

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