Portrait of Julien Cohen-Adad

Julien Cohen-Adad

Associate Academic Member
Associate Professor, Polytechnique Montréal, Electrical Engineering Department
Adjunct Professor, Université de Montréal, Department of Neuroscience
Research Topics
Medical Machine Learning
Website
Google Scholar
X

Biography

Julien Cohen-Adad is a professor at Polytechnique Montréal and the associate director of the Neuroimaging Functional Unit at Université de Montréal. He is also the Canada Research Chair in Quantitative Magnetic Resonance Imaging.

His research focuses on advancing neuroimaging methods with the help of AI. Some examples of projects are:

- Multi-modal training for medical imaging tasks (segmentation of pathologies, diagnosis, etc.)

- Adding prior from MRI physics to improve model generalization

- Incorporating uncertainty measures to deal with inter-rater variability

- Continuous learning strategies when data sharing is restricted

- Bringing AI methods into clinical radiology routine via user-friendly software solutions

Cohen-Adad also leads multiple open-source software projects that are benefiting the research and clinical community (see neuro.polymtl.ca/software.html). In short, he loves MRI with strong magnets, neuroimaging, programming and open science!

Current Students

Rohan Banerjee
Master's Research - Polytechnique Montréal
Co-supervisor :
Gauthier Gidel
Website
Github
Google Scholar
Pierre Louis Benveniste
PhD - Polytechnique Montréal
Co-supervisor :
Hervé Lombaert
Website
Github
Google Scholar
Armand Collin
PhD - Polytechnique Montréal
Github
Thomas Dagonneau
Master's Research - Polytechnique Montréal
Github
Naga Karthik Enamundram
PhD - Polytechnique Montréal
Co-supervisor :
Sarath Chandar
emvnagakarthik@gmail.com
Website
Github
Google Scholar
Nilser Laines Medina
PhD - Polytechnique Montréal
Nathan Molinier
PhD - Polytechnique Montréal
Github
Kalum Ost
Collaborating researcher
Abel Salmona Salmona
Research Intern - Polytechnique Montréal
abel.salmona@gmail.com
Github
Julien Thouveny
Master's Research - Université de Montréal
Arthur Toulouse
Master's Research - Polytechnique Montréal
Github
Jan Valosek
Postdoctorate - Polytechnique Montréal
Website
Github
Google Scholar

Publications

Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord
Masaaki Hori
Tomoko Maekawa
Kouhei Kamiya
Akifumi Hagiwara
Masami Goto
Mariko Yoshida Takemura
Shohei Fujita
Christina Andica
Koji Kamagata
Julien Cohen-Adad
Shigeki Aoki
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that… (see more) are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.
2022-02-15
Magnetic Resonance in Medical Sciences (published)
doi.org
Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord
Masaaki Hori
Tomoko Maekawa
Kouhei Kamiya
Akifumi Hagiwara
Masami Goto
Mariko Y. Takemura
Shohei Fujita
Christina Andica
Koji Kamagata
Julien Cohen-Adad
Shigeki Aoki
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that… (see more) are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.
2022-02-15
Magnetic Resonance in Medical Sciences (published)
doi.org
Erratum to: Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps (Magn Reson Med. 2022;87:781‐790.)
Fang Frank Yu
Susie Y. Huang
Ashwin S. Kumar
T. Witzel
Congyu Liao
Tanguy Duval
Julien Cohen-Adad
Berkin Bilgic
2022-02-10
Magnetic Resonance in Medicine (published)
doi.org
Erratum to: Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps (Magn Reson Med. 2022;87:781‐790.)
Fang Frank Yu
Susie Yi Huang
Ashwin Kumar
Thomas Witzel
Congyu Liao
Tanguy Duval
Julien Cohen-Adad
Berkin Bilgic
2022-02-10
Magnetic Resonance in Medicine (published)
doi.org
Titre: Title: Comparison of Myelin Imaging Techniques in Ex Vivo Spinal Cord Auteur:
Nikola Stikov
Julien Cohen-Adad
Manh-Tung Vuong
Vuong Manh Tung
Myelin is a dielectric material that wraps around the axons of nerve fibers to enable fast conduction of signals throughout the nervous syst… (see more)em. Loss of myelin can cause anywhere from minor interruption to complete disruption of nerve impulses in a range of neurodegenerative diseases such as multiple sclerosis and Parkinson’s disease. There is an ongoing debate in the myelin imaging community about which biomarker based on Magnetic Resonance Imaging (MRI) is more correlated with myelin. In this work, we implemented and compared several MRI-based myelin imaging techniques (quantitative magnetization transfer imaging, myelin water imaging, and proton density imaging) by evaluating their repeatability and their relation to large-scale histology in the ex vivo spinal cords of a rat, a dog, and a human. While there are studies investigating the relationship between pairs of them as well as with histology, to the best of our knowledge, this is the first study that implemented and compared all those methods at the same time to evaluate their reproducibility and their correlation with myelin. Qualitatively the contrasts were similar, and all techniques had comparable scan-rescan and correlations with histology. Surprisingly, the voxel-wise correlations between the various myelin measures were almost as high as the scan-rescan correlations. The correlations decreased when only white matter was considered, which could be due to the small dynamic range of the measurement, or due to artifacts related to the preparation and panoramic scanning of the tissue. We conclude that the myelin imaging techniques explored in this thesis exhibit similar specificity to myelin, yet the histological correlations suggest that more work is needed to determine the optimal myelin imaging protocol. The study also pointed out some potential miscalibrations during acquisitions as well as data processing that may lead to anywhere from minor to major impact on the accuracy of the results. These include B1 mapping, insufficient spoiling and variation of the predelay time. We have also standardized the data processing routines by upgrading qMTLab to qMRLab which adds several quantitative MR methods to the toolbox, such as standard T1 mapping and field mapping. In addition, the data of the dog spinal cord in this study will be published together with the analysis scripts to help the interested reader to reproduce the findings from this thesis.
The Myelin‐Weighted Connectome in Parkinson's Disease
Tommy Boshkovski
Julien Cohen-Adad
Bratislav Mišić
Isabelle Arnulf
Jean‐Christophe Corvol
Marie Vidailhet
Stéphane Lehéricy
Nikola Stikov
Matteo Mancini
2021-12-22
Movement Disorders (published)
doi.org
2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T
Nilser Laines Medina
Charley Gros
Julien Cohen-Adad
Virginie Callot
A. Troter
2021-10-13
ArXiv (preprint)
arxiv.org
arxiv.org
Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants
Paul Bautin
Julien Cohen-Adad
2021-10-04
NeuroImage : Clinical (published)
doi.org
Normalizing automatic spinal cord cross-sectional area measures
S. Bédard
Julien Cohen-Adad
Spinal cord cross-sectional area (CSA) is a relevant biomarker to assess spinal cord atrophy in various neurodegenerative diseases. However,… (see more) the considerable inter-subject variability among healthy participants currently limits its usage. Previous studies explored factors contributing to the variability, yet the normalization models were based on a relatively limited number of participants (typically 300 participants), required manual intervention, and were not implemented in an open-access comprehensive analysis pipeline. Another limitation is related to the imprecise prediction of the spinal levels when using vertebral levels as a reference; a question never addressed before in the search for a normalization method. In this study we implemented a method to measure CSA automatically from a spatial reference based on the central nervous system (the pontomedullary junction, PMJ), we investigated various factors to explain variability, and we developed normalization strategies on a large cohort (N=804). Cervical spinal cord CSA was computed on T1w MRI scans for 804 participants from the UK Biobank database. In addition to computing cross-sectional at the C2-C3 vertebral disc, it was also measured at 64 mm caudal from the PMJ. The effect of various biological, demographic and anatomical factors was explored by computing Pearson’s correlation coefficients. A stepwise linear regression found significant predictors; the coefficients of the best fit model were used to normalize CSA. The correlation between CSA measured at C2-C3 and using the PMJ was y = 0.98x + 1.78 (R2 = 0.97). The best normalization model included thalamus volume, brain volume, sex and interaction between brain volume and sex. With this model, the coefficient of variation went down from 10.09% (without normalization) to 8.59%, a reduction of 14.85%. In this study we identified factors explaining inter-subject variability of spinal cord CSA over a large cohort of participants, and developed a normalization model to reduce the variability. We implemented an approach, based on the PMJ, to measure CSA to overcome limitations associated with the vertebral reference. This approach warrants further validation, especially in longitudinal cohorts. The PMJ-based method and normalization models are readily available in the Spinal Cord Toolbox.
2021-10-01
bioRxiv (preprint)
doi.org
Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers
Julien Cohen-Adad
Eva Alonso‐Ortiz
Mihael Abramovic
Carina Arneitz
Nicole Atcheson
Laura Barlow
Robert L. Barry
Markus Barth
Marco Battiston
Christian Büchel
Matthew D. Budde
Virginie Callot
Anna J. E. Combes
Benjamin De Leener
Maxime Descoteaux
Paulo Loureiro de Sousa
Marek Dostál
Julien Doyon
Adam Dvorak
Falk Eippert … (see 71 more)
Karla R. Epperson
Kevin S. Epperson
Patrick Freund
Jürgen Finsterbusch
Alexandru Foias
Michela Fratini
Issei Fukunaga
Claudia A. M. Gandini Wheeler-Kingshott
Giancarlo Germani
Guillaume Gilbert
Federico Giove
Charley Gros
Francesco Grussu
Akifumi Hagiwara
Pierre-Gilles Henry
Tomáš Horák
Masaaki Hori
James Joers
Kouhei Kamiya
Haleh Karbasforoushan
Miloš Keřkovský
Ali Khatibi
Joo‐Won Kim
Nawal Kinany
Hagen H. Kitzler
Shannon Kolind
Yazhuo Kong
Petr Kudlička
Paul Kuntke
Nyoman D. Kurniawan
Slawomir Kusmia
René Labounek
Maria Marcella Lagana
Cornelia Laule
Christine S. Law
Christophe Lenglet
Tobias Leutritz
Yaou Liu
Sara Llufriu
Sean Mackey
Eloy Martinez-Heras
Loan Mattera
Igor Nestrašil
Kristin P. O’Grady
Nico Papinutto
Daniel Papp
Deborah Pareto
Todd B. Parrish
Anna Pichiecchio
Ferran Prados
Àlex Rovira
Marc J. Ruitenberg
Rebecca S. Samson
Giovanni Savini
Maryam Seif
Alan C. Seifert
Alex K. Smith
Seth A. Smith
Zachary A. Smith
Elisabeth Solana
Y. Suzuki
George Tackley
Alexandra Tinnermann
Jan Valošek
Dimitri Van De Ville
Marios C. Yiannakas
Kenneth A. Weber
Nikolaus Weiskopf
Richard G. Wise
Patrik O. Wyss
Junqian Xu
2021-09-23
Scientific Data (published)
doi.org
Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling
Reza Azad
Lucas Rouhier
Julien Cohen-Adad
2021-09-21
Machine Learning in Medical Imaging (published)
doi.org
arxiv.org
Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge
Uzay Macar
Enamundram Naga Karthik
Charley Gros
Andreanne Lemay
Julien Cohen-Adad
This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion … (see more)Segmentation. An overview of the data preprocessing steps applied is provided along with a brief description of the pipelines used, in terms of the architecture and the hyperparameters. Our code for this work can be found at: https://github.com/ivadomed/ms-challenge-2021.
2021-09-12
ArXiv (preprint)
arxiv.org
arxiv.org