In this talk, I will cover what tractography and tractometry can do well and what are some of the important current tractography limitations such as the length, position, shape and gyral biases, and will present some solutions that start addressing these limitations. The lack of ground truth on long‐range connectivity of the human brain makes it hard to quantitatively evaluate results. A key challenge for future tractography algorithms will be to control for false positives, while identifying the full extent of existing fiber bundles. Exiting new solutions are possible with microstructure-informed tractography and machine learning-based techniques.
Maxime DESCOTEAUX, PhD
is Professor in Computer Science since 2009 at the Science Faculty of Sherbrooke University. He is the founder and director of the Sherbrooke Connectivity Imaging Laboratory (SCIL) (http://scil.usherbrooke.ca/
). His research focuses on brain connectivity from state-of-the-art diffusion MRI acquisition, reconstruction, tractography, processing and visualization. The aim of the SCIL is to better understand structural connectivity, develop novel tractography algorithms, validate them and use them for human brain mapping and connectomics applications.Maxime Descoteaux did a post-doctorat fellow at NeuroSpin
under the supervision of Cyril Poupon and Denis Le Bihan. He also obtained a PhD in Computer Science at INRIA Sophia Antipolis
- Mediterranée, supervised by R. Deriche after he obtained a M.Sc under the supervision of K. Siddiqi in Computer Science
at Center for Intelligent Machines
, McGill University, where he also obtained a B.Sc, graduating from the joint honors Mathematics
and Computer Science program.
Contact Person: Gloria Menegaz