Anatomical processing streams

If your FMRIPREP output is written out in the T1w output space, it is already aligned to the preprocessed T1w image. You can send this native space preprocessed T1w into the XCP structural processing stream to

  • Register it to one of our many supplied templates (including OASIS, MNI and PNC)
  • Warp our many included atlases into the space of your BOLD data to extract time series
  • Run structural analysis on your T1w images

The XCP system includes 7 standard processing streams for volumetric anatomy. These base anatomical streams are summarized below. All processing streams are heavily based on the ANTs software library. Base anatomical streams can be modified at will to suit the dataset that is to be processed. Consult module documentation for additional details.

Processing routines

N4 bias field correction

Module: struc_

N4 bias field correction removes spatial intensity bias from the anatomical image using the N4 approach from ANTs, a variant of nonparametric nonuniform intensity normalisation.


ANTs brain extraction

Module: struc_

Products: mask

ANTs brain extraction combines a standard-space estimate of the probability that each voxel is a part of the brain (a brain parenchyma prior), a registration to standard space, and topological refinement in order to estimate the extent of the brain and remove non-brain voxels.


ANTs registration

Module: struc_

ANTs registration uses the top-performing symmetric normalisation (SyN) approach to compute a diffeomorphic function that aligns each subject’s anatomy to a sample- or population-level template brain.

[Reference 1](

[Reference 2](

Prior-guided segmentation

Module: struc_

Products: segmentation

ANTs Atropos combines Bayesian tissue-class priors in standard space with a SyN registration and a refinement step in order to produce a high-quality segmentation of the subject’s anatomy into tissue classes. Typical templates will produce a 6-class segmentation, wherein 1 corresponds to cerebrospinal fluid, 2 to cortical grey matter, 3 to cortical white matter, 4 to subcortical grey matter, 5 to cerebellum, and 6 to brainstem.


Priorless segmentation

Module: struc_

Products: segmentation

Priorless segmentation is a faster segmentation step that results in 3 tissue-class priors based on k-means clustering and refinement. For a T1-weighted image, 1 corresponds to cerebrospinal fluid, 2 corresponds to grey matter, and 3 corresponds to white matter.


DiReCT cortical thickness

Module: struc_

Products: corticalThickness

ANTs computes cortical thickness on a voxelwise basis in volumetric images using the DiReCT algorithm.


Grey matter density

Module: gmd_

Products: gmd, segmentation3class

Grey matter density is estimated as the probability that each voxel is assigned to the grey matter tissue class as determined via a k-means 3-class tissue segmentation and subsequent refinements.


Joint label fusion

Module: jlf

Products: JLF MICCAI atlas

Joint label fusion produces a custom, subject-level anatomical segmentation by diffeomorphically registering an ensemble of high-quality, manually segmented images (usually 20-40 LPBA subjects) to the subject’s anatomical image. A voting procedure is then applied in order to assign each voxel of the subject’s brain to a single region.


Regional quantification

Module: roiquant_

Regional quantification converts voxelwise derivative maps (for instance, cortical thickness and grey matter density estimates) into regional values based on any number of provided parcellations. It is implemented in the XCP system’s roiquant module.

Volume estimation

Module: roiquant

Estimates of global, regional, and tissue compartment volumes are computed as a part of regional quantification in the anatomical processing stream. It is implemented in the XCP system’s roiquant.

Quality assessment

Module: qcanat

Several indices of image quality are currently computable during anatomical processing. It is currently recommended to eschew these indices in favor of the Euler number, which has been found to perform better.



Module: [struc]

Image normalization shifts derivative maps (and potentially the primary image) into a standard sample-level or population-level space to facilitate comparisons between subjects. The normalization step applies the transformations computed in the ANTs registration step.