rmsandu / dams-dicom-anonymizer

DAMS aka DICOM-Anomymization-Mapping-Script

DAMS

DAMS aka DICOM-Anomymization-Mapping-Script

Description

These scripts perform some simple DICOM encoding (anonymization) and mapping between the source CT images and the segmentation masks that were derived from them (optional) by reading the DICOM files one by one using the PyDicom library and saving them to disk.

My data also contains XML files with patient related info in them so I anonymized those as well (see DAMSanonymizer/XML/anonymization_xml_logs.py). My anonymization is not anonymization per-se, but encoding of the patient specific details with a mapping key. Since I later need to relate the image information (radiomics) with clinical data I need a specific key for each patient. Of course, you can generate random keys or leave the tags empty (None), if you do not need to know the patient mapping. Moreover, you can also edit (anonymize) more fields.

The following DICOM metatags are encoded/anonymized:

• PatientName (0010,0010) - Encoded
• PatientID (0010,0020) - Encoded
• PatientBirthDate (0010,0030) - Encoded

The following DICOM fields were deleted (i.e. replaced with None value).

• PatientSex
• InstitutionName
• InstitutionAddress
• InstitutionalDepartmentName
• StudyName
• OperatorsName
• ReferringPhysicianName
• DeviceSerialNumber
• AcquisitionTime
• AcquisitionDateTime
• StudyTime
• ContentTime
• ContentDate

External Libraries

• PyDicom (for DICOM processing)
• Untagle (for XML files processing)

Usage for Single Patient Folder

To avoid errors keep all the files associated with 1 patient and 1 study (multiple series possible) in one folder, otherwise the de-identification will be inconsistent. The original files will be modified and also will remain grouped in their original patients, studies, and series. Careful about overwriting original raw data.

To execute the de-identification run the __main__.py script.

 python -m DAMSdicomanonymizer --i "C:\patientfolder\PAT_M001" --patient_name "MEVR001" --patient_id "R001" --patient_dob "19500101" 

or use the short names:

python -m DAMSdicomanonymizer -i "C:\Patients\Pat_M07" -n "MAVM07" -u "M07" -d "194901001"

The following functions are then called:

• get_args()
• anonymizer_dicom.encode_dcm_files_patient(args["rootdir"], args["patient_name"], args["patient_id"], args["patient_dob"])

Batch Processing (multiple patients)

This option reads an Excel file using Pandas and goes row by row in the field Patient_ID and Patient_Dir_Paths. That's the only difference from the single patient folder processing.

Output

Anonymized DICOM files re-written to disk. Careful, this script overwrites the original data. If you want to save it somewhere else, modify the scipt. PyDicom has the save_as(filepath) option for each individual DICOM slice (which I also used).

dams-dicom-anonymizer's Issues

readme reference segmentation img

source img seq readme

Add unit tests

add unit tests to check that the de-identification was performed correctly for both XMLs and DICOM