Sulcal Pits Extraction

Extraction of the sulcal pits as described in:
Auzias, G, Brun, L, Deruelle, C, Coulon, O, Deep sulcal landmarks: algorithmic and conceptual improvements in the definition and extraction of sulcal pits, NeuroImage, 2015.

Description

This pipeline computes:

Important: default parameters setting
As detailed in Auzias et al., NeuroImage 2015, the parameters have a direct influence on the results. The default values of these parameters have been set using a population of healthy adult individuals. In particular, the value of the parameter group_average_Fidler_length can be computed for your data using the process Average Mesh Fiedler Length; and the value of the parameter group_average_surface_area can be computed for your data using the process Average Mesh area

Parameters

input_mesh: Hemisphere White Mesh ( input )
side: Choice ( input )
mask_texture: Cingular pole texture ( optional, input )
DPF_alpha: Float ( input )
thresh_ridge: Float ( input )
thresh_dist: Float ( input )
group_average_Fiedler_length: Float ( input )
thresh_area: Float ( input )
group_average_surface_area: Float ( input )
DPF_texture: DPF texture ( output )
pits_texture: pits texture ( output )
noisypits_texture: noisy pits texture ( output )
ridges_texture: ridges texture ( output )
basins_texture: basins texture ( output )

Technical information

Toolbox : Cortical Surface

User level : 0

Identifier : SulcalPitsExtraction

File name : brainvisa/toolboxes/cortical_surface/processes/anatomy/SulcalPitsExtraction.py

Supported file formats :

input_mesh :
GIFTI file, GIFTI file, MESH mesh, MNI OBJ mesh, PLY mesh, TRI mesh
mask_texture :
GIFTI file, GIFTI file, Texture
DPF_texture :
GIFTI file, GIFTI file, Texture
pits_texture :
GIFTI file, GIFTI file, Texture
noisypits_texture :
GIFTI file, GIFTI file, Texture
ridges_texture :
GIFTI file, GIFTI file, Texture
basins_texture :
GIFTI file, GIFTI file, Texture