Docker Image: https://hub.docker.com/r/mohanar/facemasking
Container JSON: https://bitbucket.org/mohanar_radiologics/radiologics_containers
The identifying or sensitive anatomical features in MR and CT images used in research raise patient privacy concerns when such data are shared. The Face Masking package implements anatomical surface modification algorithm customized to de-identify MR head images that minimizes the impact on the resulting image statistics.
Face Masking obscures facial features from high-resolution MR scans that need to be shared, to reduce the research subject identification risk. Face Masking is minimally invasive and is designed to run with other processing tools such as skull stripping, MR artifact correction or segmentation.
MASKFACE_HOME=/etc/maskface
PATH=${MASKFACE_HOME}/bin:${PATH}
export PATH MASKFACE_HOMEcsh/tcsh:
setenv MASKFACE_HOME /etc/maskface
setenv PATH ${MASKFACE_HOME}/bin:${PATH}
For typical usage and running options, please refer to the mask_face usage manual page.
Read this to configure face masking as an XNAT pipeline.
Assuming ‘subject_dicom’ is a directory in current directory with a DICOM series to be defaced:
>docker pull mohanar/facemasking:v1.12
>docker run –rm -v `pwd`:/tmp mohanar/facemasking:v1.12 /bin/bash -c “cd /tmp && pwd && source /opt/facemasking_launch_script/maskface_setup.sh && mask_face_nomatlab subject_dicom -b 1 && chmod -R 777 *”
This video tutorial shows the use of the anonymization tools, DicomBrowser and Face Masking, for sharing data in XNAT.