cat-surf — Python bindings for CAT-Surface

PyPI Python License: GPL-2.0

cat-surf provides Python access to CAT-Surface, a mature C/C++ toolkit for surface-based neuroimaging analysis, focusing on the processing and analysis of cortical surface meshes.

CAT-Surface has been used internally for more than 10 years as part of CAT12, the SPM toolbox for computational anatomy. With this package, it is now distributed as an independent Python package for direct integration into external workflows to make CAT-Surface easier to use:

  • in T1Prep workflows (currently relying on CAT-Surface binaries)
  • in Python-based pipelines without subprocess-heavy wrappers
  • in reproducible environments via published wheels

Installation

pip install cat-surf

Pre-built wheels are available for:

  • macOS — arm64 (Apple Silicon), x86_64 (Intel)
  • Linux — x86_64, aarch64 (manylinux)
  • Python 3.9 – 3.13

Basic usage

import cat_surf

# Check version
print(cat_surf.__version__)

# Load a surface file (GIFTI, FreeSurfer, BIC/MNI formats)
vertices, faces = cat_surf.read_surface("lh.central.gii")

# Per-vertex area
area, total_area = cat_surf.get_area(vertices, faces)

# Euler characteristic
chi = cat_surf.euler_characteristic(vertices, faces)

# Smooth per-vertex data (heat kernel, FWHM in mm)
smoothed = cat_surf.smooth_heatkernel(vertices, faces, area, fwhm=20.0)

Surface operations

Function Description Mirrors
read_surface / write_surface Multi-format surface I/O (GIFTI, FreeSurfer, BIC) —
read_values / write_values Per-vertex scalar I/O —
get_area Per-vertex and total surface area CAT_SurfArea
get_area_normalized Area normalized by a reference sphere CAT_SurfArea -sphere
euler_characteristic Topological integrity check —
smooth_heatkernel Heat-kernel smoothing of per-vertex data —
smooth_mesh Laplacian/Taubin mesh vertex smoothing —
smoothed_curvatures Mean curvature estimation —
sulcus_depth Sulcal depth via depth potential —
reduce_mesh Quadric (QEM) mesh decimation (aggressiveness, preserve_sharp) CAT_SurfReduce
remove_intersections Self-intersection repair (MeshFix) CAT_SurfRemoveIntersections
count_intersections Count self-intersecting faces CAT_SurfRemoveIntersections -count
surf_average Vertex-wise averaging across surfaces (return_rms for std dev) CAT_SurfAverage
surf_to_sphere Inflate surface to sphere CAT_Surf2Sphere
sphere_radius Mean radius of a spherical surface —
correct_thickness_folding Folding-based thickness correction (slope, max_dist clipping) CAT_SurfCorrectThicknessFolding
point_distance Linked vertex distance between two meshes CAT_SurfDistance -link
point_distance_mean Mean (Tfs) vertex distance CAT_SurfDistance -mean
hausdorff_distance Hausdorff distance between two surfaces —
resample_to_sphere Resample surface/values onto a target sphere CAT_SurfResample
surf_deform Deform a surface toward a volume isovalue CAT_SurfDeform
surf_to_pial_white Estimate pial + white surfaces from a central surface CAT_Surf2PialWhite
central_to_pial Generate a pial surface from central + thickness —
surf_warp DARTEL-based spherical registration CAT_SurfWarp
spherical_demon Spherical Demons spherical registration CAT_SurfSphericalDemon

Volume operations

Function Description Mirrors
vol_sanlm Structure-adaptive non-local means denoising CAT_VolSanlm
vol_blood_vessel_correction Blood vessel intensity correction CAT_VolBloodVesselCorrection
vol_thickness_pbt Cortical thickness via projection-based method CAT_VolThicknessPbt
vol_amap Adaptive maximum a posteriori tissue segmentation CAT_VolAmap (core only)
vol_marching_cubes Isosurface extraction with genus-0 topology correction CAT_VolMarchingCubes
vol2surf Map a volume to a surface along inward normals CAT_Vol2Surf

Volume input convention

Every volume-consuming function accepts three input forms interchangeably:

import cat_surf, nibabel as nib
import numpy as np

# 1. file path
v, f = cat_surf.vol_marching_cubes("brain.nii.gz", threshold=0.5)

# 2. (ndarray, affine) tuple — supply a 4×4 RAS+ affine
v, f = cat_surf.vol_marching_cubes((array, affine), threshold=0.5)

# 3. any nibabel-image-like object (.affine + .get_fdata())
v, f = cat_surf.vol_marching_cubes(nib.load("brain.nii.gz"), threshold=0.5)

The data is auto-converted to float32 (matching the C library's expectation), and 4-D series default to the middle frame. No copy is made if the array is already float32/Fortran-order. Internally a minimal nifti_image is built from the affine — only the fields libCAT actually reads (dims, voxel sizes, sto_xyz).

Registration

Function Description
bbreg Full BBR pipeline: optional NMI init → boundary-based surface registration
bbreg_detect_contrast Auto-detect T1/FLAIR vs T2/BOLD from WM/GM intensity ratio
volume_register_nmi Cross-modal rigid registration via Normalised Mutual Information (≈ mri_coreg)
volume_register_robust Same-modality rigid registration via Tukey biweight M-estimation (≈ mri_robust_register)

Basic BBR usage

import cat_surf
import nibabel as nib

# Load surfaces (GIFTI or any format supported by cat_surf.read_surface)
lh_verts, lh_faces = cat_surf.read_surface("lh.white.surf.gii")
rh_verts, rh_faces = cat_surf.read_surface("rh.white.surf.gii")

# Full pipeline: NMI init from T1w reference, then BBR
matrix, cost = cat_surf.bbreg(
    "bold_mean.nii.gz",
    lh_surface=(lh_verts, lh_faces),
    rh_surface=(rh_verts, rh_faces),
    ref_file="T1w.nii.gz",     # NMI initialisation
    verbose=True,
)
print(f"BBR cost: {cost:.4f}")
print("EPI → T1 matrix:\n", matrix)

# Save the 4×4 transform for use with FSL / ANTs
import numpy as np
np.savetxt("epi_to_t1.txt", matrix)

Standalone volume registration

# Cross-modal (EPI ↔ T1w) — NMI
matrix, nmi = cat_surf.volume_register_nmi("T1w.nii.gz", "bold_mean.nii.gz")

# Same-modality (T1w ↔ T1w) — robust IRLS
matrix, res = cat_surf.volume_register_robust("t1_ref.nii.gz", "t1_moving.nii.gz")

Contrast auto-detection

# 0 = T1/FLAIR, 1 = T2/BOLD, -1 = undetermined
contrast = cat_surf.bbreg_detect_contrast(
    "bold_mean.nii.gz",
    lh_surface=(lh_verts, lh_faces),
    rh_surface=(rh_verts, rh_faces),
)

Conversion utilities

Function Description
arrays_to_polygons Convert NumPy vertex/face arrays to internal polygon mesh
polygons_to_arrays Convert internal polygon mesh back to NumPy arrays

cat_surf.cli — drop-in replacement for the CAT binaries

The cat_surf.cli subpackage mirrors the CAT_* command-line binaries one-to-one: same names (snake_case, CAT_ prefix dropped), same positional argument order, same option semantics and defaults. Each function reads its inputs from disk, calls the in-memory numpy wrapper, and writes the outputs — ideal for porting shell scripts to Python.

from cat_surf import cli

# CAT_VolMarchingCubes brain.nii.gz brain.gii -thresh 0.5
cli.vol_marching_cubes("brain.nii.gz", "brain.gii", threshold=0.5)

# CAT_Surf2PialWhite central.gii thickness.txt labels.nii pial.gii white.gii
cli.surf2pial_white("central.gii", "thickness.txt", "labels.nii",
                    "pial.gii", "white.gii")

# CAT_SurfDistance -mean surf1.gii surf2.gii out.txt
cli.surf_distance("surf1.gii", "surf2.gii", "out.txt", mode="mean")

# CAT_VolSanlm in.nii out.nii -strength 1.0
cli.vol_sanlm("in.nii", "out.nii", strength=1.0)

The full mapping:

CAT binary cat_surf.cli.<name>
CAT_Surf2PialWhite surf2pial_white
CAT_Surf2Sphere surf2sphere
CAT_SurfArea surf_area
CAT_SurfAverage surf_average
CAT_SurfCorrectThicknessFolding surf_correct_thickness_folding
CAT_SurfDeform surf_deform
CAT_SurfDistance surf_distance
CAT_SurfReduce surf_reduce
CAT_SurfRemoveIntersections surf_remove_intersections
CAT_SurfResample surf_resample
CAT_SurfSphericalDemon surf_spherical_demon
CAT_SurfWarp surf_warp
CAT_Vol2Surf vol2surf
CAT_VolAmap vol_amap
CAT_VolBloodVesselCorrection vol_blood_vessel_correction
CAT_VolMarchingCubes vol_marching_cubes
CAT_VolSanlm vol_sanlm
CAT_VolThicknessPbt vol_thickness_pbt

For composable in-memory pipelines, prefer the lower-level cat_surf API directly — the CLI shims are just thin convenience wrappers.

Typical use cases

  • Cortical mesh processing (resampling, smoothing, metrics)
  • Thickness and folding related computations
  • Volume-to-surface projection
  • Denoising and volume preprocessing for structural MRI

Citation / provenance

If you use this package in research, please cite Dahnke et al., 2013 and mention the cat-surf package version for reproducibility.

Source

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Avatar for ChristianGaser from gravatar.com ChristianGaser

Credits

Author: Christian Gaser

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