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


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

Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Research Intern - Polytechnique Montréal
Master's Research - Université de Montréal
Postdoctorate - Polytechnique Montréal


Automatic Segmentation of the Spinal Cord Nerve Rootlets
Jan Valošek
Theo Mathieu
Raphaëlle Schlienger
Olivia S. Kowalczyk
Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal co… (see more)rd. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 +- 0.16 (mean +- standard deviation across rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation= 1.41 %), as well as low inter-session variability (coefficient of variation= 1.30 %) indicating stable predictions across different MRI
Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy
Abdul Al-Shawwa
Kalum Ost
David Anderson
Newton Cho
Nathan Evaniew
W. Bradley Jacobs
Allan R. Martin
Ranjeet Gaekwad
Saswati Tripathy
Jacques Bouchard
Steven Casha
Roger Cho
Stephen duPlessis
Peter Lewkonia
Fred Nicholls
Paul T. Salo
Alex Soroceanu
Ganesh Swamy
Kenneth C. Thomas
Michael M.H. Yang … (see 2 more)
David W. Cadotte
A database of the healthy human spinal cord morphometry in the PAM50 template space
Jan Valošek
Sandrine Bédard
Miloš Keřkovský
Tomáš Rohan
Abstract Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of … (see more)spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
Russell A. Poldrack
Christopher J. Markiewicz
Stefan Appelhoff
Yoni K. Ashar
Tibor Auer
Sylvain Baillet
Shashank Bansal
Leandro Beltrachini
Christian G. Benar
C. Bénar
Giacomo Bertazzoli
Suyash Bhogawar
Ross W. Blair
Marta Bortoletto
Mathieu Boudreau
Teon L. Brooks
Vince D. Calhoun
Filippo Maria Castelli
Patricia Clement
Alexander L. Cohen … (see 100 more)
Sasha D’Ambrosio
Gilles de Hollander
María de la Iglesia-Vayá
Alejandro de la Vega
Arnaud Delorme
Orrin Devinsky
Dejan Draschkow
Eugene Paul Duff
E. Duff
Elizabeth DuPre
Eric Earl
Oscar Esteban
Franklin W. Feingold
Guillaume Flandin
Anthony Galassi
Giuseppe Gallitto
Melanie Ganz
Rémi Gau
James Gholam
Sulagna Dia Ghosh
Satrajit S. Ghosh
Alessio Giacomel
Ashley G. Gillman
Padraig Gleeson
Alexandre Gramfort
Samuel Guay
Giacomo Guidali
Yaroslav O. Halchenko
Daniel A. Handwerker
Nell Hardcastle
Peer Herholz
Dora Hermes
Christopher J. Honey
C. Honey
Robert B. Innis
Horea-Ioan Ioanas
Andrew Jahn
Agâh Karakuzu
David B. Keator
Gregory Kiar
Balint Kincses
Angela R. Laird
Jonathan C. Lau
Alberto Lazari
Jon Haitz Legarreta
Adam Li
Xiangrui Li
Bradley C. Love
Hanzhang Lu
Eleonora Marcantoni
Camille Maumet
Giacomo Mazzamuto
Steven L. Meisler
Mark Mikkelsen
Henk Mutsaerts
Thomas E. Nichols
Aki Nikolaidis
Gustav Nilsonne
Guiomar Niso
Martin Norgaard
Thomas W. Okell
Robert Oostenveld
Eduard Ort
Patrick J. Park
Mateusz Pawlik
Cyril R. Pernet
Franco Pestilli
Jan Petr
Christophe Phillips
Jean-Baptiste Poline
Luca Pollonini
P. Raamana
Pradeep Reddy Raamana
Petra Ritter
Gaia Rizzo
Kay A. Robbins
Alexander P. Rockhill
Christine Rogers
Ariel Rokem
Chris Rorden
Alexandre Routier
Jose Manuel Saborit-Torres
Taylor Salo
Michael Schirner
Robert E. Smith
Tamas Spisak
Julia Sprenger
Nicole C. Swann
Martin Szinte
Sylvain Takerkart
Bertrand Thirion
Adam G. Thomas
Sajjad Torabian
Gael Varoquaux
Bradley Voytek
Julius Welzel
Martin Wilson
Tal Yarkoni
Krzysztof J. Gorgolewski
SCIseg: Automatic Segmentation of T2-weighted Hyperintense Lesions in Spinal Cord Injury
Enamundram Naga Karthik
Jan Valošek
Andrew C. Smith
Dario Pfyffer
Simon Schading-Sassenhausen
Lynn Farner
Kenneth A. Weber
Patrick Freund
Background: Quantitative MRI biomarkers in spinal cord injury (SCI) can help understand the extent of the focal injury. However, due to the … (see more)lack of automatic segmentation methods, these biomarkers are derived manually, which is a time-consuming process prone to intra- and inter-rater variability, thus limiting large multi-site studies and translation to clinical workflows. Purpose: To develop a deep learning tool for the automatic segmentation of T2-weighted hyperintense lesions and the spinal cord in SCI patients. Material and Methods: This retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023 who underwent clinical MRI examination. A deep learning model, SCIseg, was trained on T2-weighted images with heterogeneous image resolutions (isotropic, anisotropic), and orientations (axial, sagittal) acquired using scanners from different manufacturers (Siemens, Philips, GE) and different field strengths (1T, 1.5T, 3T) for the automatic segmentation of SCI lesions and the spinal cord. The proposed method was visually and quantitatively compared with other open-source baseline methods. Quantitative biomarkers (lesion volume, lesion length, and maximal axial damage ratio) computed from manual ground-truth lesion masks and automatic SCIseg predictions were correlated with clinical scores (pinprick, light touch, and lower extremity motor scores). A between-group comparison was performed using the Wilcoxon signed-rank test. Results: MRI data from 191 SCI patients (mean age, 48.1 years {+/-} 17.9 [SD]; 142 males) were used for training. Compared to existing methods, SCIseg achieved the best segmentation performance for both the cord and lesions and generalized well to both traumatic and non-traumatic SCI patients. SCIseg is open-source and accessible through the Spinal Cord Toolbox. Conclusion: Automatic segmentation of intramedullary lesions commonly seen in traumatic SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model generalizes across lesion etiologies (traumatic, ischemic), scanner manufacturers and heterogeneous image resolutions.
Influence of scanning plane on Human Spinal Cord functional Magnetic Resonance echo planar imaging
Marta Moraschi
Silvia Tommasin
Laura Maugeri
Mauro Dinuzzo
Marco Masullo
Fabio Mangini
Lorenzo Giovannelli
Daniele Mascali
Tommaso Gili
Valerio Pisani
Ugo Md Nocentini
Federico Giove
Michela Fratini
BACKGROUND: Functional Magnetic Resonance Imaging (fMRI) is based on the Blood Oxygenation Level Dependent contrast and has been exploited f… (see more)or the indirect study of the neuronal activity within both the brain and the spinal cord. However, the interpretation of spinal cord fMRI (scfMRI) is still controversial and its diffusion is rather limited because of technical limitations. Overcoming these limitations would have a beneficial effect for the assessment and follow-up of spinal injuries and neurodegenerative diseases. PURPOSE: This study was aimed at systematically verify whether sagittal scanning in scfMRI using EPI readout is a viable alternative to the more common axial scanning, and at optimizing a pipeline for EPI-based scfMRI data analysis, based on Spinal Cord Toolbox (SCT). METHODS: Forty-five healthy subjects underwent MRI acquisition in a Philips Achieva 3T MRI scanner. T2*-weighted fMRI data were acquired using a GE-EPI sequence along sagittal and axial planes during an isometric motor task. Differences on benchmarks were assessed via paired two-sample t-test at p=0.05. RESULTS: We investigated the impact of the acquisition strategy by means of various metrics such as Temporal Signal to Noise Ratio (tSNR), Dice Coefficient to assess geometric distortions, Reproducibility and Sensitivity. tSNR was higher in axial than in sagittal scans, as well as reproducibility within the whole cord mask (t=7.4, p0.01) and within the GM mask (t=4.2, p0.01). The other benchmarks, associated with distortion and functional response, showed no differenc
Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox.
Jan Valošek
Towards contrast-agnostic soft segmentation of the spinal cord
Sandrine Bédard
Enamundram Naga Karthik
Charidimos Tsagkas
Emanuele Pravatà
Cristina Granziera
Andrew C. Smith
Kenneth Arnold Weber
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and … (see more)monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants (
Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord
Haykel Snoussi
Olivier Commowick
Benoit Combes
Elise Bannier
Slimane Tounekti
Anne Kerbrat
Christian Barillot
Emmanuel Caruyer
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is… (see more) widely used for MS diagnosis and clinical follow‐up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball‐and‐Stick models.
Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord
Kurt G Schilling
Anna J. E. Combes
Karthik Ramadass
Francois Rheault
Grace Sweeney
Logan Prock
Subramaniam Sriram
John C Gore
Bennett A Landman
Seth A. Smith
Kristin P. O’Grady
Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment acro… (see more)ss time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality.
Pontomedullary junction as a reference for spinal cord cross-sectional area: validation across neck positions
Sandrine Bédard
Maxime Bouthillier
An 8‐channel Tx dipole and 20‐channel Rx loop coil array for MRI of the cervical spinal cord at 7 Tesla
Nibardo Lopez‐Rios
Kyle M. Gilbert
Daniel Papp
Gaspard Cereza
Alexandru Foias
Deshpande Rangaprakash
Markus W. May
Bastien Guerin
Lawrence L. Wald
Boris Keil
Jason P. Stockmann
Robert L. Barry
The quality of cervical spinal cord images can be improved by the use of tailored radiofrequency (RF) coil solutions for ultrahigh field ima… (see more)ging; however, very few commercial and research 7‐T RF coils currently exist for the spinal cord, and in particular, those with parallel transmission (pTx) capabilities. This work presents the design, testing, and validation of a pTx/Rx coil for the human neck and cervical/upper thoracic spinal cord. The pTx portion is composed of eight dipoles to ensure high homogeneity over this large region of the spinal cord. The Rx portion is made up of twenty semiadaptable overlapping loops to produce high signal‐to‐noise ratio (SNR) across the patient population. The coil housing is designed to facilitate patient positioning and comfort, while also being tight fitting to ensure high sensitivity. We demonstrate RF shimming capabilities to optimize B1+ uniformity, power efficiency, and/or specific absorption rate efficiency. B1+ homogeneity, SNR, and g‐factor were evaluated in adult volunteers and demonstrated excellent performance from the occipital lobe down to the T4‐T5 level. We compared the proposed coil with two state‐of‐the‐art head and head/neck coils, confirming its superiority in the cervical and upper thoracic regions of the spinal cord. This coil solution therefore provides a convincing platform for producing the high image quality necessary for clinical and research scanning of the upper spinal cord.