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CorticoMetrics FreeSurfer conversion tools

Note: FreeSurfer is a set of software tools for brain segmentation of MRI data. As such, non-brain imaging applications do not apply, and the tasks defined for DICOM4QI SEG component cannot be addressed. However, DICOM SEG is applicable, and functionality is included to encode brain region segmentations. Therefore, the platform is included.

1. Description of the platform/product:

   • name and version of the software: fs2dicom v0.1.2, which works with FreeSurfer 6.0
   • free?: yes; FreeSurfer is available here, and fs2dicom is available for download from PyPI
   • commercial?: no
   • open source?: yes; FreeSurfer and fs2dicom source code are hosted on GitHub
   • what DICOM library do you use?: DCMQI to write DICOM-SEG

2. Description of the relevant features of the platform: FreeSurfer's subcortical segmentation results can be converted to DICOM-SEG using fs-aseg.json. This mapping was contributed by Emily Lindemer.

   • are both single and multiple segments supported?: Yes, however FreeSurfer's subcortical segmentation process assumes a mutually exclusive label set.
   • do you support both BINARY and FRACTIONAL segmentation types? BINARY only.
   • do you support compressed objects? I beleive itkimage2segimage can read compressed itk objects but does not write compressed DICOM objects(?)
   • do you render the segment using the color specified in the DICOM object? N/A (write-only), however FreeSurfer's recommended color scheme is preserved.
   • how do you communicate segment semantics to the user? The fs-aseg.json maps FreeSurfer's subcortical labels to SNOMED codes.
   • how do you support the user in defining the semantics of the object at the time segmentation is created? FreeSurfer's subcortical label set is predefined, so this is pre-computed.

3. Read task: Not supported

4. Write task:

   • Download the file fs2dicom-rsna2018-example-v2.tar.gz by selecting the "Download -> Direct download" menu option from here. This contains contains the following, as well as files to create a DICOM Structured Report:
     • ./dicom-anon: Directory containting input DICOMs for a FreeSurfer-compatible T1 weighted MPRAGE sequence.
     • ./fs2dicom-rsna2018-example/fs-subjects/fs2dicom-rsna2018ex/: Directory containing the output of the above DICOMs processed with FreeSurfer 6.0 subcortical segmentations
     • ./aseg-seg.dcm: The DICOM seg image created by fs2dicom
     • ./readme.txt: Description of these contents, and instructions on how to run fs2dicom on the results
   • Running the example (NOTE: docker and Python >= 3.4 are required to run this. See the readme.txt and the fs2dicom GitHub page for more details):

     pip install fs2dicom   # if not installed previously
     tar zxvf ./fs2dicom-rsna2018-example-v2.tar.gz
     cd fs2dicom-rsna2018-example
     
     fs2dicom create-seg \
       ./dicoms-anon/MR.1.3.12.2.1107.5.2.32.35162.1999123112191238865917962 \
       ./fs-subjects/fs2dicom-rsna2018ex/mri/aseg.mgz   \
       ./aseg-seg.dcm
     

   • Verifying the output:

     dciodvfy ./aseg-seg.dcm