Software

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio [dot] devicofallani [at] gmail [dot] com and mario [dot] chavez [at] upmc [dot] fr

Reference: https://sites.google.com/site/fr2eborn/download

 

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley [dot] durrleman [at] inria [dot] fr

Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.

Sacha

logo_sacha_v1_transSACHA (Segmentation Automatisée Compétitive de l’Hippocampe et de l’Amygdale) is a software dedicated to the joint segmentation of the hippocampus and the amygdala from 3D-T1 MRI brain scans ([1], [2]) with prior knowledge on the location of the hippocampus and the amygdala derived from a probabilistic atlas and relative positions with respect to automatically identified anatomical landmarks. This method has been validated by comparison with manual tracing in healthy controls, patients with Alzheimer’s disease and patients with epilepsy ([1], [2]). It has also been successfully applied to over 5,000 MRI scans in patients with various conditions ([3], [4]).

Contacts: marie [dot] chupin [at] upmc [dot] fr

References:

  1. Chupin M et al. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage 46:749-761, 2009.
  2. Chupin M et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer’s disease. Neuroimage 34:996-1019, 2007.
  3. Chupin M et al. Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19:579-587, 2009.
  4. Colliot O et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248:194-201, 2008.

 

Whasa

WHASAWHASA (White matter Hyperintensities Automated Segmentation Algorithm) is an algorithm for the automated segmentation of White Matter Hyperintensities (WMH). This approach segments hyperintensities from T2-FLAIR and 3D T1 MRI brain scans. The method has been validated by comparison with manual tracings in 67 patients acquired on 6 different 1.5T MRI scanners with various sequences as used in clinical routine (slice thickness on FLAIR images about 5mm). It has also been applied to over 1000 patients from various centres with both 1.5 and 3T MRI scanners.

Contacts: marie [dot] chupin [at] upmc [dot] fr and ludovic [dot] fillon [at] upmc [dot] fr

Reference: T. Samaille, L. Fillon et al. Contrast-Based Fully Automatic Segmentation of White Matter Hyperintensities: Method and Validation. PLoS ONE. 7(11): e48953, 2012.