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
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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

Armand Collin
Maîtrise recherche - Polytechnique Montréal
armand.collin@mila.quebec
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Jan Valosek
Postdoctorat - Polytechnique Montréal
jan.valosek@mila.quebec
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Julien Thouveny
Maîtrise recherche - Université de Montréal
julien.thouveny@mila.quebec
Louis-François Bouchard
Doctorat - Polytechnique Montréal
louis-francois.bouchard@mila.quebec
Site web
Github
Naga Karthik Enamundram
Doctorat - Polytechnique Montréal
Co-superviseur⋅e :
Sarath Chandar Anbil Parthipan
naga-karthik.enamundram@mila.quebec
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Github
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Nathan Molinier
Doctorat - Polytechnique Montréal
nathan.molinier@mila.quebec
Github
Nilser Laines Medina
Doctorat - Polytechnique Montréal
nilser.lainesmedina@mila.quebec
Pierre Louis Benveniste
Doctorat - Polytechnique Montréal
pierre-louis.benveniste@mila.quebec
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Github
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Rohan Banerjee
Doctorat - Polytechnique Montréal
rohan.banerjee@mila.quebec
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Github
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Simon Queric
Stagiaire de recherche - Polytechnique Montréal
simon.queric@mila.quebec
Github

Publications

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data
Marie-Hélène Bourget
L. Kamentsky
Satrajit S. Ghosh
Giacomo Mazzamuto
Alberto Lazari
Christopher J. Markiewicz
Robert Oostenveld
Guiomar Niso
Yaroslav O. Halchenko
Ilona Lipp
Sylvain Takerkart
P. Toussaint
Ali Raza Khan
Gustav Nilsonne
Filippo Maria Castelli
Julien Cohen-Adad
The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusab… (voir plus)le way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.
2022-04-19
Frontiers in Neuroscience (published)
doi.org
NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline
Vicente Enguix
Jeanette K. Kenley
David Luck
Julien Cohen-Adad
G. Lodygensky
Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its … (voir plus)integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.
2022-04-08
ArXiv (preprint)
doi.org
arxiv.org
Rapid, automated nerve histomorphometry through open-source artificial intelligence
Simeon Christian Daeschler
Marie-Hélène Bourget
Dorsa Derakhshan
Vasudev Sharma
Stoyan Ivaylov Asenov
Tessa Gordon
Julien Cohen-Adad
Gregory Howard Borschel
2022-04-08
Scientific Reports (publié)
doi.org
Rapid, automated nerve histomorphometry through open-source artificial intelligence
S. Daeschler
Marie-Hélène Bourget
Dorsa Derakhshan
Vasudev Sharma
Stoyan Ivaylov Asenov
Tessa Gordon
Julien Cohen-Adad
G. Borschel
2022-04-08
Scientific Reports (published)
doi.org
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Reza Azad
Moein Heidari
Julien Cohen-Adad
Ehsan Adeli
Dorit Merhof
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related disea… (voir plus)ses such as osteoporosis, vertebral fractures, and intervertebral disc herniation. To date, various approaches have been developed in the literature which routinely relies on detecting the discs as the primary step. A disadvantage of many cohort studies is that the localization algorithm also yields false-positive detections. In this study, we aim to alleviate this problem by proposing a novel U-Net-based structure to predict a set of candidates for intervertebral disc locations. In our design, we integrate the image shape information (image gradients) to encourage the model to learn rich and generic geometrical information. This additional signal guides the model to selectively emphasize the contextual representation and suppress the less discriminative features. On the post-processing side, to further decrease the false positive rate, we propose a permutation invariant 'look once' model, which accelerates the candidate recovery procedure. In comparison with previous studies, our proposed approach does not need to perform the selection in an iterative fashion. The proposed method was evaluated on the spine generic public multi-center dataset and demonstrated superior performance compared to previous work. We have provided the implementation code in https://github.com/rezazad68/intervertebral-lookonce
2022-04-06
ArXiv (preprint)
doi.org
arxiv.org
Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter
Julien Cohen-Adad
Eva Alonso‐Ortiz
Stephanie Alley
Maria Marcella Lagana
Francesca Baglio
Signe Johanna Vannesjo
Haleh Karbasforoushan
Maryam Seif
Alan C. Seifert
Junqian Xu
Joo‐Won Kim
René Labounek
Lubomír Vojtíšek
Marek Dostál
Jan Valošek
Rebecca S. Samson
Francesco Grussu
Marco Battiston
Claudia A. M. Gandini Wheeler-Kingshott
Marios C. Yiannakas … (voir 4 de plus)
Guillaume Gilbert
Torben Schneider
Brian Johnson
Ferran Prados
Spinal cord gray‐matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare… (voir plus) various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter.
2022-04-05
Magnetic Resonance in Medicine (publié)
doi.org
Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter
Julien Cohen-Adad
Eva Alonso‐Ortiz
Stephanie Alley
M. M. Laganá
Francesca Baglio
S. Vannesjo
Haleh Karbasforoushan
Maryam Seif
A. Seifert
Junqian Xu
Joo-won Kim
René Labounek
Lubomír Vojtíšek
Marek Dostál
Jan Valošek
Rebecca Sara Samson
Francesco Grussu
Marco Battiston
C. G. Gandini Wheeler-Kingshott
Marios C. Yiannakas … (voir 4 de plus)
Guillaume Gilbert
Torben Schneider
Brian Johnson
Ferran Prados
Spinal cord gray‐matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare… (voir plus) various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter.
2022-04-05
Magnetic Resonance in Medicine (published)
doi.org
Quantitative electrophysiological assessments as predictive markers of lower limb motor recovery after spinal cord injury: a pilot study with an adaptive trial design
Yin Nan Huang
El-Mehdi Meftah
Charlotte H. Pion
Jean-Marc Mac-Thiong
Julien Cohen-Adad
Dorothy Barthélemy
2022-02-24
Spinal Cord Series and Cases (publié)
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… (voir plus) 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 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… (voir plus) 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 (publié)
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 (publié)
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