Author

Marc-Alexandre Côté

Université de SherbrookeORCID 0000-0002-5147-7859Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Neuroscience· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:benchmark study· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Jean‐Christophe HoudeiD· 3 shared Klaus Hermann Maier-HeiniD· 3 shared Maxime DescoteauxiD· 3 shared Peter NeheriD· 3 shared Laurent PetitiD· 2 shared Aldo QuattroneiD· 1 shared Alessandro DaducciiD· 1 shared Alessia SaricaiD· 1 shared

Activity by Year

2017-2017 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersNeuroscience

Jean‐Christophe HoudeORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Tractography-based connectomes are dominated by false-positive connections

Klaus Hermann Maier-Hein, Peter Neher, Jean‐Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, jinyu zhong, Maxime Chamberland, Fang-Cheng F. Yeh, Ying‐Chia Lin, Qing Ji, Wilburn E. Reddick, John O. Glass, David Qixiang Chen, Yuanjing Feng, Chengfeng Gao, , Maxime Descoteaux

The paper evaluates tractography methods by analyzing a simulated dataset, finding that most algorithms generate many false-positive connections.

Nature Communications · Nov 1, 2017Peer-reviewedNeuroscience

Fiber tractography using machine learning

Peter Neher, Marc-Alexandre Côté, Jean‐Christophe Houde, Maxime Descoteaux, Klaus Hermann Maier-Hein

This paper introduces a machine learning-based fiber tractography method using random forest classification and evaluates it against state-of-the-art approaches using phantom and in vivo experiments.

NeuroImage · Jul 15, 2017Peer-reviewedNeuroscience

Learn to Track: Deep Learning for Tractography

Philippe Poulin, Marc-Alexandre Côté, Jean‐Christophe Houde, Laurent Petit, Peter Neher, Klaus Hermann Maier-Hein, Hugo Larochelle, Maxime Descoteaux

This paper applies deep learning to fiber tractography, showing models can generate streamlines from DWI data and outperform traditional methods in accuracy and false connection control.

Lecture notes in computer science · Jan 1, 2017Peer-reviewedNeuroscience