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image_burnvoxels

  1. Burning Voxels: The module takes binary masks and “burns” or imprints their voxels onto an anatomical image. This means that the areas defined by the masks are marked or labeled on the anatomical image.
  2. Output Images: The module generates multiple output images:
  • One image with all masks burned onto the anatomical image.
  • Separate images for each mask burned onto the anatomical image. If there are N masks, there will be N images.
  1. Label Values: The burned voxels are assigned label values:
  • The minimum label value is 25.
  • Subsequent label values are incremented by a dynamically determined step based on the number of masks. In summary, this module processes an anatomical image by applying binary masks to it, generating multiple labeled images as output.

Keywords : Image Processing, Voxel Burning, Mask Application


Format : tuple val(meta), val(masks), path(anat)

TypeDescriptionMandatoryPattern
metamapGroovy Map containing sample information e.g. [ id:'sample1', single_end:false ]True
maskslistList of binary masks to be burned onto the anatomical image.True*.nii.gz
anatfileAnatomical image to burn the masks onto.True*.nii.gz

Format : tuple val(meta), val(*__all.nii.gz)

TypeDescriptionMandatoryPattern
metamapGroovy Map containing sample information e.g. [ id:'sample1', single_end:false ]True
*__all.nii.gzmapAnatomical image with all masks burned onto it.True*.nii.gz

Format : tuple val(meta), val(*__*_[0-9]*[0-9].nii.gz)

TypeDescriptionMandatoryPattern
metamapGroovy Map containing sample information e.g. [ id:'sample1', single_end:false ]True
___[0-9]*[0-9].nii.gzmapAnatomical image with each mask burned onto it.True*__*_[0-9]*[0-9].nii.gz

Format : path(versions.yml)

TypeDescriptionMandatoryPattern
versions.ymlfileFile containing software versionsTrueversions.yml

DescriptionDOI
ANTsAdvanced Normalization Tools (ANTs) for image processing.10.1038/s41598-021-87564-6
MRtrix3MRtrix3 is a software package for various types of diffusion imaging data, including diffusion-weighted, diffusion-tensor, and q-ball imaging.10.1016/j.neuroimage.2019.116137
scilpyThe Sherbrooke Connectivity Imaging Lab (SCIL) Python dMRI processing toolbox.


Last updated : 2025-10-30