example of sulcal depth estimation in slam

# Authors: Guillaume Auzias <guillaume.auzias@univ-amu.fr>

# License: MIT
# sphinx_gallery_thumbnail_number = 2

This is a tutorial for computing sulcal depth using the method called DPF* introduced in the publication: Dieudonné, M., Auzias, G., Lefèvre, J., “Scale-invariant brain morphometry: application to sulcal depth”, 2026, https://arxiv.org/abs/2501.05436

import numpy

import numpy as np
# importation of slam modules
import slam.io as sio
import slam.sulcal_depth as sdepth

loading an examplar mesh and corresponding texture

mesh_file = "../examples/data/example_mesh.gii"
texture_file = "../examples/data/example_texture.gii"
mesh = sio.load_mesh(mesh_file)

The dpf* can be computed using a single function. The outuput of this function are dpf_star, a texture (vetcor of size equal to the number of vertices in the mesh) corresponding to the normalized sulcal depth; lc, the estimated characteristic length of the mesh

dpf_star, lc = sdepth.dpf_star(mesh)

Calculating vertex normals .... Please wait
Finished calculating vertex normals
Calculating curvature tensors ... Please wait
Finished Calculating curvature tensors
Calculating Principal Components ... Please wait
Finished Calculating principal components
  Computing Laplacian
    Computing mesh weights of type conformal
    -edge length threshold needed for  0  values =  0.0  %
    -number of Nan in weights:  0  =  0.0  %
    -number of Negative values in weights:  936  =  6.706792777300086  %
    -nb Nan in Laplacian :  0
    -nb Inf in Laplacian :  0

The characteristic length can then be used to compute the absolute DPF*:

print("Estimated characteristic length", lc)
abs_dpf_star = lc * dpf_star[0]

Estimated characteristic length 33.72085038911362

Compare the two with simple stats

print("mean(dpf_star)=", np.mean(dpf_star[0]))
print("std(dpf_star)=", np.std(dpf_star))
print("mean(abs_dpf_star)=", np.mean(abs_dpf_star))
print("std(abs_dpf_star)=", np.std(abs_dpf_star))

mean(dpf_star)= -0.0002800891961447778
std(dpf_star)= 0.0037244469169856434
mean(abs_dpf_star)= -0.009444845878805154
std(abs_dpf_star)= 0.12559151726986836

See the article following the link provided at the top of this example for more details about the method and its potential applications.

VISUALIZATION USING plotly

import slam.plot as splt

display_settings = {}
display_settings['colorbar_label'] = 'dpf_star'
mesh_data = {}
mesh_data['vertices'] = mesh.vertices
mesh_data['faces'] = mesh.faces
mesh_data['title'] = 'dpf_star'
intensity_data = {}
intensity_data['values'] = dpf_star[0]
intensity_data["mode"] = "vertex"
fig = splt.plot_mesh(
    mesh_data=mesh_data,
    intensity_data=intensity_data,
    display_settings=display_settings)
fig.show()
fig