Portrait de Julien Cohen-Adad

Julien Cohen-Adad

Membre académique associé
Professeur agrégé, Polytechnique Montréal, Département de génie électrique
Professeur asssocié, Université de Montréal, Département de neurosciences
Sujets de recherche
Apprentissage automatique médical
Site web
Google Scholar
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Biographie

Julien Cohen-Adad est professeur à Polytechnique Montréal et directeur associé de l'Unité de neuro-imagerie fonctionnelle de l'Université de Montréal. Il est également titulaire de la Chaire de recherche du Canada en imagerie par résonance magnétique quantitative. Ses recherches portent sur l'avancement des méthodes de neuro-imagerie avec l'aide de l'IA. Voici quelques exemples de ses projets :

- Formation multimodale pour les tâches d'imagerie médicale (segmentation des pathologies, diagnostic, etc.);

- Ajout d'un a priori issu de la physique de l'IRM pour améliorer la généralisation des modèles;

- Incorporation de mesures d'incertitude pour traiter la variabilité interévaluateurs;

- Stratégies d'apprentissage continu lorsque le partage des données est restreint;

- Introduction des méthodes d'IA dans la routine de la radiologie clinique par l’intermédiaire de solutions logicielles conviviales.

Le professeur Cohen-Adad dirige également de nombreux projets de logiciels libres qui profitent à la communauté scientifique et clinique. Plus de détails sur https://neuro.polymtl.ca/software.html.

En résumé, Julien aime : l'IRM avec des aimants puissants, la neuro-imagerie, la programmation et la science ouverte!

Étudiants actuels

Rohan Banerjee
Maîtrise recherche - Polytechnique
Co-superviseur⋅e :
Gauthier Gidel
Site web
Github
Google Scholar
Pierre Louis Benveniste
Doctorat - Polytechnique
Co-superviseur⋅e :
Hervé Lombaert
Site web
Github
Google Scholar
Armand Collin
Doctorat - Polytechnique
Github
Thomas Dagonneau
Maîtrise recherche - Polytechnique
Github
Naga Karthik Enamundram
Doctorat - Polytechnique
Co-superviseur⋅e :
Sarath Chandar
emvnagakarthik@gmail.com
Site web
Github
Google Scholar
Nilser Laines Medina
Doctorat - Polytechnique
Nathan Molinier
Doctorat - Polytechnique
Github
Kalum Ost
Collaborateur·rice de recherche
Abel Salmona Salmona
Stagiaire de recherche - Polytechnique
abel.salmona@gmail.com
Github
Julien Thouveny
Maîtrise recherche - UdeM
Arthur Toulouse
Maîtrise recherche - Polytechnique
Github
Jan Valosek
Postdoctorat - Polytechnique
Site web
Github
Google Scholar

Publications

Monitoring morphometric drift in lifelong learning segmentation of the spinal cord
Enamundram Naga Karthik
Sandrine B'edard
Jan Valovsek
Christoph Aigner
Elise Bannier
Josef Bednavr'ik
Virginie Callot
Anna Combes
Armin Curt
Gergely David
Falk Eippert
Lynn Farner
M. G. Fehlings
Patrick Freund
Tobias Granberg
Cristina Granziera
Rhscir Network Imaging Group
Ulrike Horn
Tom'avs Hor'ak
Suzanne Humphreys … (voir 36 de plus)
Markus Hupp
Anne Kerbrat
Nawal Kinany
Shannon Kolind
Petr Kudlivcka
Anna Lebret
Lisa Eunyoung Lee
Caterina Mainero
Allan R. Martin
Megan McGrath
Govind Nair
Kristin P. O’Grady
Jiwon Oh
Russell Ouellette
Nikolai Pfender
Dario Pfyffer
P. Pradat
Alexandre Prat
Emanuele Pravatà
D. S. Reich
Ilaria Ricchi
Naama Rotem-Kohavi
Simon Schading-Sassenhausen
Maryam Seif
Andrew C. Smith
Seth Aaron Smith
Grace Sweeney
Roger Tam
Anthony Traboulsee
Constantina A. Treaba
Charidimos Tsagkas
Zachary Vavasour
Dimitri Van De Ville
Kenneth A. Weber
Sarath Chandar
Julien Cohen-Adad
2025-05-02
ArXiv (prépublication)
arxiv.org
arxiv.org
Impact of through‐slice gradient optimization for dynamic slice‐wise shimming in the cervico‐thoracic spinal cord
Arnaud Breheret
Alexandre D'Astous
Yixin Ma
Jason P. Stockmann
Julien Cohen-Adad
2025-05-01
Magnetic Resonance in Medicine (publié)
doi.org
Rootlets-based registration to the spinal cord PAM50 template
Sandrine B'edard
Jan Valovsek
Valeria Oliva
Kenneth A. Weber
Julien Cohen-Adad
2025-04-30
ArXiv (prépublication)
arxiv.org
arxiv.org
Spinal Cord Tract Integrity in Degenerative Cervical Myelopathy.
Newton Cho
Abdul Al-Shawwa
W. Bradley Jacobs
Nathan Evaniew
Jacques Bouchard
Steven Casha
Stephan duPlessis
Peter Lewkonia
Fred Nicholls
Alex Soroceanu
Ganesh Swamy
Kenneth C. Thomas
Michael M.H. Yang
Julien Cohen-Adad
David W. Cadotte
2025-04-03
Neurosurgery (publié)
doi.org
Addressing Missing Modality Challenges in MRI Images: A Comprehensive Review
Reza Azad
Mohammad Dehghanmanshadi
Nika Khosravi
Julien Cohen-Adad
Dorit Merhof
2025-04-01
Computational Visual Media (publié)
doi.org
Normalizing Spinal Cord Compression Measures in Degenerative Cervical Myelopathy.
Sandrine Bédard
Jan Valošek
Maryam Seif
Armin Curt
Simon Schading-Sassenhausen
Nikolai Pfender
P. Freund
Markus Hupp
Julien Cohen-Adad
2025-03-01
The spine journal (publié)
doi.org
Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 -- Ex vivo imaging: data processing, comparisons with microscopy, and tractography
Kurt G Schilling
Amy F. D. Howard
Francesco Grussu
Andrada Ianus
Brian Hansen
Rachel L. C. Barrett
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Lucas Soustelle
Christien Bowman
Benjamin C. Tendler … (voir 38 de plus)
Andreea Hertanu
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Adam Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’ Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Ileana O. Jelescu
2025-02-26
Magnetic Resonance in Medicine (publié)
doi.org
arxiv.org
Automatic segmentation of spinal cord lesions in MS: A robust tool for axial T2-weighted MRI scans
Enamundram Naga Karthik
Julian McGinnis
Ricarda Wurm
Sebastian Ruehling
Robert Graf
Jan Valošek
Pierre-Louis Benveniste
Markus Lauerer
Jason Talbott
Rohit Bakshi
Shahamat Tauhid
Timothy Shepherd
Achim Berthele
Claus Zimmer
Bernhard Hemmer
Daniel Rueckert
Benedikt Wiestler
Jan S. Kirschke
Julien Cohen-Adad
Mark Mühlau
Deep learning models have achieved remarkable success in segmenting brain white matter lesions in multiple sclerosis (MS), becoming integral… (voir plus) to both research and clinical workflows. While brain lesions have gained significant attention in MS research, the involvement of spinal cord lesions in MS is relatively understudied. This is largely owed to the variability in spinal cord magnetic resonance imaging (MRI) acquisition protocols, high individual anatomical differences, the complex morphology and size of spinal cord lesions - and lastly, the scarcity of labeled datasets required to develop robust segmentation tools. As a result, automatic segmentation of spinal cord MS lesions remains a significant challenge. Although some segmentation tools exist for spinal cord lesions, most have been developed using sagittal T2-weighted (T2w) sequences primarily focusing on cervical spines. With the growing importance of spinal cord imaging in MS, axial T2w scans are becoming increasingly relevant due to their superior sensitivity in detecting lesions compared to sagittal acquisition protocols. However, most existing segmentation methods struggle to effectively generalize to axial sequences due to differences in image characteristics caused by the highly anisotropic spinal cord scans. To address these challenges, we developed a robust, open-source lesion segmentation tool tailored specifically for axial T2w scans covering the whole spinal cord. We investigated key factors influencing lesion segmentation, including the impact of stitching together individually acquired spinal regions, straightening the spinal cord, and comparing the effectiveness of 2D and 3D convolutional neural networks (CNNs). Drawing on these insights, we trained a multi-center model using an extensive dataset of 582 MS patients, resulting in a dataset comprising an entirety of 2,167 scans. We empirically evaluated the model's segmentation performance across various spinal segments for lesions with varying sizes. Our model significantly outperforms the current state-of-the-art methods, providing consistent segmentation across cervical, thoracic and lumbar regions. To support the broader research community, we integrate our model into the widely-used Spinal Cord Toolbox (v7.0 and above), making it accessible via the command sct_deepseg -task seg_sc_ms_lesion_axial_t2w -i .
2025-01-23
medRxiv (prépublication)
doi.org
Automatic segmentation of spinal cord lesions in MS: A robust tool for axial T2-weighted MRI scans
Enamundram Naga Karthik
J. McGinnis
R. Wurm
S. Ruehling
R. Graf
Jan Valošek
Pierre-Louis Benveniste
M. Lauerer
J. Talbott
R. Bakshi
S. Tauhid
T. Shepherd
A. Berthele
C. Zimmer
B. Hemmer
D. Rueckert
B. Wiestler
J. Kirschke
Julien Cohen-Adad
M. Muehlau
Deep learning models have achieved remarkable success in segmenting brain white matter lesions in multiple sclerosis (MS), becoming integral… (voir plus) to both research and clinical workflows. While brain lesions have gained significant attention in MS research, the involvement of spinal cord lesions in MS is relatively understudied. This is largely owed to the variability in spinal cord magnetic resonance imaging (MRI) acquisition protocols, high individual anatomical differences, the complex morphology and size of spinal cord lesions - and lastly, the scarcity of labeled datasets required to develop robust segmentation tools. As a result, automatic segmentation of spinal cord MS lesions remains a significant challenge. Although some segmentation tools exist for spinal cord lesions, most have been developed using sagittal T2-weighted (T2w) sequences primarily focusing on cervical spines. With the growing importance of spinal cord imaging in MS, axial T2w scans are becoming increasingly relevant due to their superior sensitivity in detecting lesions compared to sagittal acquisition protocols. However, most existing segmentation methods struggle to effectively generalize to axial sequences due to differences in image characteristics caused by the highly anisotropic spinal cord scans. To address these challenges, we developed a robust, open-source lesion segmentation tool tailored specifically for axial T2w scans covering the whole spinal cord. We investigated key factors influencing lesion segmentation, including the impact of stitching together individually acquired spinal regions, straightening the spinal cord, and comparing the effectiveness of 2D and 3D convolutional neural networks (CNNs). Drawing on these insights, we trained a multi-center model using an extensive dataset of 582 MS patients, resulting in a dataset comprising an entirety of 2,167 scans. We empirically evaluated the model's segmentation performance across various spinal segments for lesions with varying sizes. Our model significantly outperforms the current state-of-the-art methods, providing consistent segmentation across cervical, thoracic and lumbar regions. To support the broader research community, we integrate our model into the widely-used Spinal Cord Toolbox (v7.0 and above), making it accessible via the command sct_deepseg -task seg_sc_ms_lesion_axial_t2w -i .
2025-01-23
medRxiv (prépublication)
doi.org
Multi-center benchmarking of cervical spinal cord RF coils for 7 T MRI: A traveling spines study
Eva Alonso‐Ortiz
Daniel Papp
Robert L. Barry
Kyota Poëti
Alan C. Seifert
Kyle M. Gilbert
Nibardo Lopez‐Rios
Jan Paska
Falk Eippert
Nikolaus Weiskopf
Laura Beghini
Nadine Graedel
Robert Trampel
Martina F Callaghan
Christoph S. Aigner
Patrick Freund
Maryam Seif
Aurélien Destruel
Virginie Callot
Johanna Vannesjo … (voir 1 de plus)
Julien Cohen-Adad
2025-01-17
bioRxiv (prépublication)
doi.org
Multi-center benchmarking of cervical spinal cord RF coils for 7 T MRI: A traveling spines study
Eva Alonso‐Ortiz
Daniel Papp
Robert L. Barry
Kyota Poëti
Alan C. Seifert
Kyle M. Gilbert
Nibardo Lopez‐Rios
Jan Paska
Falk Eippert
N. Weiskopf
Laura Beghini
Nadine Graedel
Robert Trampel
M. F. Callaghan
Christoph S Aigner
Patrick Freund
Maryam Seif
A. Destruel
Virginie Callot
Johanna Vannesjo … (voir 1 de plus)
Julien Cohen-Adad
Purpose The depth within the body, small diameter, long length, and varying tissue surrounding the spinal cord impose specific consideration… (voir plus)s when designing radiofrequency coils. The optimal coil configuration for 7 T cervical spinal cord MRI is unknown and, currently, there are very few coil options. The purpose of this work was (1) to establish a quality control protocol for evaluating 7 T cervical spinal cord coils and (2) to use that protocol to evaluate the performance of 4 different coil designs. Methods Three healthy volunteers and a custom anthropomorphic phantom (the traveling spines cohort) were scanned at seven 7 T imaging centers using a common protocol and each center’s specific cervical spinal cord coil. Four different coil designs were tested (two in-house, one Rapid Biomedical, and one MRI.TOOLS design). Results The Rapid Biomedical coil was found to have the highest B1+ efficiency, whereas one of the in-house designs (NeuroPoly Lab) had the highest SNR and the largest spinal cord coverage. The MRI.TOOLS coil had the most uniform B1+ profile along the cervical spinal cord; however, it was limited in its ability to provide the requested flip angles (especially for larger individuals). The latter was also the case for the second in-house coil (MSSM). Conclusion The results of this study serve as a guide for the spinal cord MRI community in selecting the most suitable coil based on specific requirements and offer a standardized protocol for assessing future coils.
2025-01-17
bioRxiv (prépublication)
doi.org
EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data
Rohan Banerjee
Merve Kaptan
Alexandra Tinnermann
Ali Khatibi
Alice Dabbagh
Christian Büchel
Christine S.W. Law
Christian W. Kündig
Csw Law
Dario Pfyffer
David J. Lythgoe
Dimitra Tsivaka
Dimitri Van De Ville
Falk Eippert
Fauziyya Muhammad
Gary H. Glover
Gergely David
Grace Haynes
Jan Haaker
Jonathan C. W. Brooks … (voir 23 de plus)
Jürgen Finsterbusch
Katherine T. Martucci
Kimberly J. Hemmerling
Mahdi Mobarak-Abadi
Mark A. Hoggarth
Matthew A. Howard
Molly G. Bright
Nawal Kinany
Olivia S. Kowalczyk
Patrick Freund
Robert L. Barry
Sean Mackey
Shahabeddin Vahdat
Simon Schading
Stephen B. McMahon
Todd Parish
Véronique Marchand-Pauvert
Yufen Chen
Kenneth A. Weber
Zachary A. Smith
KA Weber
Benjamin De Leener
Julien Cohen-Adad
Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Prepro… (voir plus)cessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.0, and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared to other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.
2025-01-10
bioRxiv (prépublication)
doi.org