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BrainVISA is a modular an customizable software platform built to host heterogeneous tools dedicated to neuroimaging research. Many toolboxes have already been developped for BrainVISA (T1 MRI, sulcal identification and morphometry, cortical surface analysis, diffusion imaging and tractography, fMRI, nuclear imaging, EEG and MEG, TMS, histology and autoradiography, etc.).

BrainVISA main features are:
– Harmonization of communications between different software. For instance, BrainVISA toolboxes are using home-made software but also third-party software such as FreeSurfer, FSL, SPM, nipy, R-project, Matlab, etc.
– Ontology-based data organization allowing database sharing and automation of mass of data analysis.
– Fusion and interactive visualization of multimodal data (using Anatomist software).
– Automatic generation of graphical user interfaces.
– Workflow monitoring and data quality checking.
– Full customization possible.
– Runs on Linux, Mac and Windows.


Morphologist is a neuroimaging software dedicated to cortical analysis and sulcal morphometry. See and the toolbox documentation in BrainVISA.


WHASA (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.




SACHA (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


  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.