2018 |Meta Tracts: Parsimonious multi-resolution representations for modeling,visualizing and statistically analyzing brain tractograms

Parsimonious multi-resolution representations for modeling,visualizing and statistically analyzing brain tractograms

Action line: Axe DataSense

Researcher : Pierre Roussillon
Leading Researchers : Pietro Gori (Project coordinator), Jean-Marc Thiery, Damien Rohmer
Supporting Researchers : Isabelle Bloch, Tamy Boubekeur, Marie-Paule Cani
Host Laboratories : IMAGES, LTCI and STREAM, LIX


Tractography from diffusion-weighted magnetic resonance imaging (DW-MRI) is the only non-invasive technique able to trace in vivo the wiring architecture of the human brain white matter. It is widely employed for both clinical and research purposes. It results in a tractogram which is a bundle of 3D polylines, usually called fibers or streamlines, which are estimates of the trajectories of large groups of neural tracts. Recent methods produce up to one million fibers, which makes it difficult to store, visualize and process them. Every fiber “carries” an important quantity of information that goes beyond its trajectory through the voxels of the MR image. It connects two different areas of the brain and one can map voxel-wise functional signals onto it such as brain activity time series (e.g., MEG, fMRI), metabolic imaging data (e.g.,PET, MR spectroscopy) or quantities describing the microstructure of the brain (e.g., Fractional Anisotropy). Geometry, connectivity and functional signals have been shown to be crucial in the characterization of the pathophysiological processes underlying a condition (i.e., tumor) or a disease.

Goal of the project

Propose a new parsimonious and multi-resolution geometric representation, called Neural Meta Tracts (NMTs), for fast, robust and reliable processing, comparison and visualization of white matter tractograms. The proposed representation will carry all the attributes of the original fibers, namely geometry, connectivity and functional signals.

Medical applications

  • Interactive and comprehensive visualization of NMTs. The user will be able to map different structural and functional data onto the NMTs
  • Automatic segmentation of NMTs into anatomically-relevant and reproducible white matter tracts using anatomical prior knowledge
  • Statistical shape/functional analysis of NMTs for group comparison between healthy subjects and patients



  • Interview published in the I’MTech, the science and technology news website of the Institut Mines-Telecom: LINK

Scientific production

  • C. Mercier, S. Rousseau, P. Gori, I. Bloch, T. Boubekeur. QFib: Fast and Efficient Brain Tractogram Compression. Neuroinformatics. 2020
  • J. Feydy, P. Roussillon, A. Trouvé and P. Gori. Fast and Scalable Optimal Transport for Brain Tractograms. In: MICCAI. 2019
  • P. Roussillon, J.M. Thiery, I. Bloch and P. Gori. Appariement difféomorphique robuste de faisceaux neuronaux . In: GRETSI. 2019
  • A. Delmonte, C. Mercier, J. Pallud, I. Bloch and P. Gori. White matter multi-resolution segmentation using fuzzy set theory . In: IEEE ISBI. 2019
  • S. Rousseau, C. Mercier, P. Gori, I. Bloch and T. Boubekeur . QFib: Fast and Accurate Compression of White Matter Tractograms . In: OHBM. 2019
  • A. Delmonte, I. Bloch, D. Hasboun, C. Mercier, J. Pallud and P. Gori. Segmentation of White Matter Tractograms Using Fuzzy Spatial Relations. In: OHBM. 2018
  • C. Mercier, P. Gori, D. Rohmer, M-P. Cani, T. Boubekeur, J-M. Thiery and I. Bloch. Progressive and Efficient Multi-Resolution Representations for Brain Tractograms. In: EG VCBM. 2018