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

Histology-informed automatic parcellation of white matter tracts in the rat spinal cord
Harris Nami
Christian S. Perone
Julien Cohen-Adad
The white matter is organized into “tracts” or “bundles,” which connect different parts of the central nervous system. Knowing where… (voir plus) these tracts are located in each individual is important for understanding the cause of potential sensorial, motor or cognitive deficits and for developing appropriate treatments. Traditionally, tracts are found using tracer injection, which is a difficult, slow and poorly scalable technique. However, axon populations from a given tract exhibit specific characteristics in terms of morphometrics and myelination. Hence, the delineation of tracts could, in principle, be done based on their morphometry. The objective of this study was to generate automatic parcellation of the rat spinal white matter tracts using the manifold information from scanning electron microscopy images of the entire spinal cord. The axon morphometrics (axon density, axon diameter, myelin thickness and g-ratio) were computed pixelwise following automatic axon segmentation using AxonSeg. The parcellation was based on an agglomerative clustering algorithm to group the tracts. Results show that axon morphometrics provide sufficient information to automatically identify some white matter tracts in the spinal cord, however, not all tracts were correctly identified. Future developments of microstructure quantitative MRI even bring hope for a personalized clustering of white matter tracts in each individual patient. The generated atlas and the associated code can be found at https://github.com/neuropoly/tract-clustering.
2022-11-29
Frontiers in Neuroanatomy (publié)
doi.org
Shimming toolbox: An open‐source software toolbox for <scp>B0</scp> and <scp>B1</scp> shimming in MRI
Alexandre D'Astous
Gaspard Cereza
Daniel Papp
Kyle M. Gilbert
Jason P. Stockmann
Eva Alonso‐Ortiz
Julien Cohen-Adad
2022-11-28
Magnetic Resonance in Medicine (publié)
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
2022-09-16
Predictive Intelligence in Medicine (publié)
doi.org
arxiv.org
Relationship Between Arterial Stiffness Index, Pulse Pressure, and Magnetic Resonance Imaging Markers of White Matter Integrity: A UK Biobank Study
Atef Badji
Julien Cohen-Adad
Hélène Girouard
2022-06-21
Frontiers in Aging Neuroscience (publié)
doi.org
NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline
Vicente Enguix
Jeanette Kenley
David Luck
Julien Cohen-Adad
Gregory Anton 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.
2022-06-17
Frontiers in Neuroinformatics (publié)
doi.org
arxiv.org
Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI
Agah Karakuzu
Labonny Biswas
Julien Cohen-Adad
Nikola Stikov
Purpose We developed an end-to-end workflow that starts with a vendor-neutral acquisition and tested the hypothesis that vendor-neutral sequ… (voir plus)ences decrease inter-vendor variability of T1, MTR and MTsat measurements. Methods We developed and deployed a vendor-neutral 3D spoiled gradient-echo (SPGR) sequence on three clinical scanners by two MRI vendors. We then acquired T1 maps on the ISMRM-NIST system phantom, as well as T1, MTR and MTsat maps in three healthy participants. We performed hierarchical shift function analysis in vivo to characterize the differences between scanners when the vendor-neutral sequence is used instead of commercial vendor implementations. Inter-vendor deviations were compared for statistical significance to test the hypothesis. Results In the phantom, the vendor-neutral sequence reduced inter-vendor differences from 8 - 19.4% to 0.2 - 5% with an overall accuracy improvement, reducing ground truth T1 deviations from 7 - 11% to 0.2 - 4%. In vivo we found that the variability between vendors is significantly reduced (p = 0.015) for all maps (T1, MTR and MTsat) using the vendor-neutral sequence. Conclusion We conclude that vendor-neutral workflows are feasible and compatible with clinical MRI scanners. The significant reduction of inter-vendor variability using vendor-neutral sequences has important implications for qMRI research and for the reliability of multicenter clinical trials.
2022-06-03
Magnetic Resonance in Medicine (publié)
doi.org
Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study
Ali Khatibi
Shahabeddin Vahdat
Ovidiu Lungu
Jürgen Finsterbusch
Christian Büchel
Julien Cohen-Adad
V. Marchand-Pauvert
Julien Doyon
2022-06-01
NeuroImage (publié)
doi.org
Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data
Marie-Hélène Bourget
Lee Kamentsky
Satrajit S. Ghosh
Giacomo Mazzamuto
Alberto Lazari
Christopher J. Markiewicz
Robert Oostenveld
Guiomar Niso
Yaroslav O. Halchenko
Ilona Lipp
Sylvain Takerkart
Paule-Joanne Toussaint
Ali R. Khan
Gustav Nilsonne
Filippo Maria Castelli
Stefan Ross Eric Franklin Anthony Rémi Christopher J. Taylor Appelhoff
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 (publié)
doi.org
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