mask_face usage manual

Version Release Notes

First version: 08.24.2012
Current version: 10.15.2018

Fixed flipping in output DICOM’s, affecting MaskFace.12.27.2017.nomatlab.lin64.zip distribution.
A Docker image supporting XNAT 1.7 container service is released. Docker image: https://hub.docker.com/r/mohanar/facemasking. Container service command: https://bitbucket.org/mohanar_radiologics/radiologics_containers

New in version 12.27.2017:

A version that works using Matlab Runtime Environment (no Matlab license required) is released.
Fixes to work correctly on Ubuntu machines

New in version 01.24.2014:

Reworked the structure of the script into procedure-based
The script now accepts multiple scans to process with the same parameters.
The following modes of face masking are allowed by script
1. based on FSL-coregistered coordinates file (generated by fsl after coregistration)
2. based on supplied ROI coordinate file
3. based on explicitly supplied ROI coordinates
4. based on the reference scan
each masking region requires a separate run of mask_surf_auto.m
3D snapshots
tweaked brain mask application to exclude more boundary voxels
better support of anisotropic voxels
added ear masking

major updates to the face masking xnat pipeline:
- new parameters:
  - ref: reference scan
  - use_manual_roi: signal that manual ROI will be used instead of auto-registration.
  - rois: specification of manual ROI’s
  - dirs: ROI normal direction (s)
- step 0: scan lists are converted to be used with xnat2loc
- step 1: xnat2loc is now used to download and process DICOM scans instead of XNATRestClient.
- new face masking script takes a list of scans, reference and ROI parameters as input.

Usage

CODE

mask_face <image>[,<image>..] [options]

The first argument must list one or more MR head image files. If more than one image is specified, you must also specify one of them as reference with -r option. The reference image will be used for spatial co-registration with an atlas, and others will use the reference facial mask.

By default, the input is the DICOM directory of a single series. You can also supply images in Analyze/NIFTI (.hdr/.img pair) format by adding -a option. The output and input formats are the same.

Options

For the complete set of options, run mask_face without parameters.

Flag	Description
-a	Required if you are supplying images in Analyze/NIFTI (.hdr/.img pair) format
-m <method>	Method used (coating, blur, normfilter, all): coating: sets all voxels in the face layer to the single average intensity blur: performs the entire volume blur, and limit the effect to the facial region normfilter: (recommended) a fine-tuned method that will preserve most of the intensity distribution all: produces output by all three methods
-t <threshold>	Mask threshold: Threshold is selected automatically (recommended), but can be changed with this option
-s <grid_step>	Grid Step: Larger grid step will result in coarser looking 3D renderings, and tends to modify more voxels outside of the immediate near-surface
-v <#>	Control the amount of intermediate output: 0: only saves the resulting output 2: will generate the 3D volumes for boundary layer, boundary mask, and “flattened” face (both blurred and unblurred)
-b	Generate the brain mask used by FSL's bet algorithm prior to face masking and use it to exclude brain voxels from modification
-e <#>	Controls ear-masking. 1: Mask ears
-um <#>	Toggles the use of manual ROI coordinates 1: Use manual ROI coordinates specified with either -roi and -ver or -rf
-r <image>	Required if more than one image is being defaced. The reference image will be used for spatial co-registration with an atlas, and others will use the reference facial mask. (Use the highest quality image for your reference.)

References

The algorithm main paper:

Milchenko, M. V. & Marcus, D. (2012). Obscuring surface anatomy in volumetric imaging data. Neuroinformatics. doi: 10.1007/s12021-012-9160-3