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
X

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

Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy
Abdul Al-Shawwa
Michael Craig
Kalum Ost
David Anderson
Steven Casha
W. Bradley Jacobs
Nathan Evaniew
Saswati Tripathy
Jacques Bouchard
Peter Lewkonia
Fred Nicholls
Alex Soroceanu
Ganesh Swamy
Kenneth C. Thomas
Stephan duPlessis
Michael M.H. Yang
Julien Cohen-Adad
Nicholas Dea
Jefferson R. Wilson
David W. Cadotte
2025-01-01
BMJ Neurology Open (publié)
doi.org
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
Julien Cohen-Adad
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and … (voir plus)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 (
2025-01-01
Medical Image Anal. (publié)
doi.org
arxiv.org
SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.
Enamundram Naga Karthik
Jan Valošek
Andrew C. Smith
Dario Pfyffer
Simon Schading-Sassenhausen
Lynn Farner
KA Weber
Kenneth A. Weber
Patrick Freund
Julien Cohen-Adad
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This ar… (voir plus)ticle will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023 from 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 males). The data consisted of T2-weighted MRI acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic and lumbar spine. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg and contrast-agnostic, all part of the Spinal Cord Toolbox). Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results SCIseg achieved a Dice score of 0.92 ± 0.07 (mean ± SD) and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and extracted relevant lesion biomarkers (namely, lesion volume, lesion length, and maximal axial damage ratio). SCIseg is open-source and accessible through the Spinal Cord Toolbox (v6.2 and above). Published under a CC BY 4.0 license.
2024-11-06
Radiology: Artificial Intelligence (publié)
doi.org
SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.
Enamundram Naga Karthik
Jan Valošek
Andrew C. Smith
Dario Pfyffer
Simon Schading-Sassenhausen
Lynn Farner
KA Weber
Patrick Freund
Julien Cohen-Adad
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This ar… (voir plus)ticle will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023 from 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 males). The data consisted of T2-weighted MRI acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic and lumbar spine. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg and contrast-agnostic, all part of the Spinal Cord Toolbox). Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results SCIseg achieved a Dice score of 0.92 ± 0.07 (mean ± SD) and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and extracted relevant lesion biomarkers (namely, lesion volume, lesion length, and maximal axial damage ratio). SCIseg is open-source and accessible through the Spinal Cord Toolbox (v6.2 and above). Published under a CC BY 4.0 license.
2024-11-06
Radiology: Artificial Intelligence (publié)
doi.org
Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future
B Mark Keegan
Martina Absinta
Julien Cohen-Adad
Eoin P Flanagan
Roland G Henry
Eric C Klawiter
Shannon Kolind
Stephen Krieger
Cornelia Laule
John A Lincoln
Steven Messina
Jiwon Oh
Nico Papinutto
Seth Aaron Smith
Anthony Traboulsee
2024-11-06
Brain Communications (publié)
doi.org
Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future
B Mark Keegan
Martina Absinta
Julien Cohen-Adad
Eoin P Flanagan
Roland G Henry
Eric C Klawiter
Shannon Kolind
Stephen Krieger
Cornelia Laule
John A Lincoln
Steven Messina
Jiwon Oh
Nico Papinutto
Seth Aaron Smith
Anthony Traboulsee
Abstract Spinal cord disease is important in most people with multiple sclerosis, but assessment remains less emphasized in patient care, ba… (voir plus)sic and clinical research and therapeutic trials. The North American Imaging in Multiple Sclerosis Spinal Cord Interest Group was formed to determine and present the contemporary landscape of multiple sclerosis spinal cord evaluation, further existing and advanced spinal cord imaging techniques, and foster collaborative work. Important themes arose: (i) multiple sclerosis spinal cord lesions (differential diagnosis, association with clinical course); (ii) spinal cord radiological–pathological associations; (iii) ‘critical’ spinal cord lesions; (iv) multiple sclerosis topographical model; (v) spinal cord atrophy; and (vi) automated and special imaging techniques. Distinguishing multiple sclerosis from other myelopathic aetiology is increasingly refined by imaging and serological studies. Post-mortem spinal cord findings and MRI pathological correlative studies demonstrate MRI’s high sensitivity in detecting microstructural demyelination and axonal loss. Spinal leptomeninges include immune inflammatory infiltrates, some in B-cell lymphoid-like structures. ‘Critical’ demyelinating lesions along spinal cord corticospinal tracts are anatomically consistent with and may be disproportionately associated with motor progression. Multiple sclerosis topographical model implicates the spinal cord as an area where threshold impairment associates with multiple sclerosis disability. Progressive spinal cord atrophy and ‘silent’ multiple sclerosis progression may be emerging as an important multiple sclerosis prognostic biomarker. Manual atrophy assessment is complicated by rater bias, while automation (e.g. Spinal Cord Toolbox), and artificial intelligence may reduce this. Collaborative research by the North American Imaging in Multiple Sclerosis and similar groups with experts combining distinct strengths is key to advancing assessment and treatment of people with multiple sclerosis spinal cord disease.
2024-11-06
Brain Communications (publié)
doi.org
Spiral volumetric optoacoustic tomography of reduced oxygen saturation in the spinal cord of M83 mouse model of Parkinson's disease.
Benjamin F. Combes
Sandeep Kumar Kalva
Pierre-Louis Benveniste
Agathe Tournant
Man Hoi Law
Joshua Newton
Maik Krüger
Rebecca Z. Weber
Inês Dias
Daniela Noain
Xose Luis Dean-Ben
Uwe Konietzko
Christian R. Baumann
Per-Göran Gillberg
Christoph Hock
Roger M. Nitsch
Julien Cohen-Adad
Daniel Razansky
Ruiqing Ni
2024-10-09
European Journal of Nuclear Medicine and Molecular Imaging (publié)
doi.org
Spiral volumetric optoacoustic tomography of reduced oxygen saturation in the spinal cord of M83 mouse model of Parkinson’s disease
Benjamin F. Combes
Sandeep Kumar Kalva
Pierre-Louis Benveniste
Agathe Tournant
Man Hoi Law
Joshua Newton
Maik Krüger
Rebecca Z Weber
Inês Dias
Daniela Noain
Xose Luis Dean-Ben
Uwe Konietzko
Christian R. Baumann
Per-Göran Gillberg
Christoph Hock
Roger M. Nitsch
Julien Cohen-Adad
Daniel Razansky
Ruiqing Ni
2024-10-09
European Journal of Nuclear Medicine and Molecular Imaging (publié)
doi.org
<scp>RF</scp> shimming in the cervical spinal cord at <scp>7 T</scp>
Daniel Papp
Kyle M. Gilbert
Gaspard Cereza
Alexandre D'Astous
Nibardo Lopez‐Rios
Mathieu Boudreau
Marcus J. Couch
Pedram Yazdanbakhsh
Robert L. Barry
Eva Alonso‐Ortiz
Julien Cohen-Adad
2024-08-13
Magnetic Resonance in Medicine (publié)
doi.org
RF shimming in the cervical spinal cord at 7 T.
Daniel Papp
Kyle M. Gilbert
Gaspard Cereza
Alexandre D'Astous
Nibardo Lopez‐Rios
Mathieu Boudreau
Marcus J. Couch
Pedram Yazdanbakhsh
Robert L. Barry
Eva Alonso‐Ortiz
Julien Cohen-Adad
PURPOSE Advancing the development of 7 T MRI for spinal cord imaging is crucial for the enhanced diagnosis and monitoring of various neurode… (voir plus)generative diseases and traumas. However, a significant challenge at this field strength is the transmit field inhomogeneity. Such inhomogeneity is particularly problematic for imaging the small, deep anatomical structures of the cervical spinal cord, as it can cause uneven signal intensity and elevate the local specific absorption ratio, compromising image quality. This multisite study explores several RF shimming techniques in the cervical spinal cord. METHODS Data were collected from 5 participants between two 7 T sites with a custom 8Tx/20Rx parallel transmission coil. We explored two radiofrequency (RF) shimming approaches from an MRI vendor and four from an open-source toolbox, showcasing their ability to enhance transmit field and signal homogeneity along the cervical spinal cord. RESULTS The circularly polarized (CP), coefficient of variation (CoV), and specific absorption rate (SAR) efficiency shim modes showed the highest B1 + efficiency, and the vendor-based "patient" and "volume" modes showed the lowest B1 + efficiency. The coefficient of variation method produced the highest CSF/spinal cord contrast on T2*-weighted scans (ratio of 1.27 ± 0.03), and the lowest variation of that contrast along the superior-inferior axis. CONCLUSION The study's findings highlight the potential of RF shimming to advance 7 T MRI's clinical utility for central nervous system imaging by enabling more homogenous and efficient spinal cord imaging. Additionally, the research incorporates a reproducible Jupyter Notebook, enhancing the study's transparency and facilitating peer verification.
2024-08-13
Magnetic Resonance in Medicine (publié)
doi.org
RF shimming in the cervical spinal cord at 7 T.
Daniel Papp
Kyle M. Gilbert
Gaspard Cereza
Alexandre D'Astous
Nibardo Lopez‐Rios
Mathieu Boudreau
Marcus J. Couch
Pedram Yazdanbakhsh
Robert L. Barry
Eva Alonso‐Ortiz
Julien Cohen-Adad
PURPOSE Advancing the development of 7 T MRI for spinal cord imaging is crucial for the enhanced diagnosis and monitoring of various neurode… (voir plus)generative diseases and traumas. However, a significant challenge at this field strength is the transmit field inhomogeneity. Such inhomogeneity is particularly problematic for imaging the small, deep anatomical structures of the cervical spinal cord, as it can cause uneven signal intensity and elevate the local specific absorption ratio, compromising image quality. This multisite study explores several RF shimming techniques in the cervical spinal cord. METHODS Data were collected from 5 participants between two 7 T sites with a custom 8Tx/20Rx parallel transmission coil. We explored two radiofrequency (RF) shimming approaches from an MRI vendor and four from an open-source toolbox, showcasing their ability to enhance transmit field and signal homogeneity along the cervical spinal cord. RESULTS The circularly polarized (CP), coefficient of variation (CoV), and specific absorption rate (SAR) efficiency shim modes showed the highest B1 + efficiency, and the vendor-based "patient" and "volume" modes showed the lowest B1 + efficiency. The coefficient of variation method produced the highest CSF/spinal cord contrast on T2*-weighted scans (ratio of 1.27 ± 0.03), and the lowest variation of that contrast along the superior-inferior axis. CONCLUSION The study's findings highlight the potential of RF shimming to advance 7 T MRI's clinical utility for central nervous system imaging by enabling more homogenous and efficient spinal cord imaging. Additionally, the research incorporates a reproducible Jupyter Notebook, enhancing the study's transparency and facilitating peer verification.
2024-08-13
Magnetic Resonance in Medicine (publié)
doi.org
RF shimming in the cervical spinal cord at 7 T.
Daniel Papp
Kyle M. Gilbert
Gaspard Cereza
Alexandre D'Astous
Nibardo Lopez‐Rios
Mathieu Boudreau
Marcus J. Couch
Pedram Yazdanbakhsh
Robert L. Barry
Eva Alonso‐Ortiz
Julien Cohen-Adad
PURPOSE Advancing the development of 7 T MRI for spinal cord imaging is crucial for the enhanced diagnosis and monitoring of various neurode… (voir plus)generative diseases and traumas. However, a significant challenge at this field strength is the transmit field inhomogeneity. Such inhomogeneity is particularly problematic for imaging the small, deep anatomical structures of the cervical spinal cord, as it can cause uneven signal intensity and elevate the local specific absorption ratio, compromising image quality. This multisite study explores several RF shimming techniques in the cervical spinal cord. METHODS Data were collected from 5 participants between two 7 T sites with a custom 8Tx/20Rx parallel transmission coil. We explored two radiofrequency (RF) shimming approaches from an MRI vendor and four from an open-source toolbox, showcasing their ability to enhance transmit field and signal homogeneity along the cervical spinal cord. RESULTS The circularly polarized (CP), coefficient of variation (CoV), and specific absorption rate (SAR) efficiency shim modes showed the highest B1 + efficiency, and the vendor-based "patient" and "volume" modes showed the lowest B1 + efficiency. The coefficient of variation method produced the highest CSF/spinal cord contrast on T2*-weighted scans (ratio of 1.27 ± 0.03), and the lowest variation of that contrast along the superior-inferior axis. CONCLUSION The study's findings highlight the potential of RF shimming to advance 7 T MRI's clinical utility for central nervous system imaging by enabling more homogenous and efficient spinal cord imaging. Additionally, the research incorporates a reproducible Jupyter Notebook, enhancing the study's transparency and facilitating peer verification.
2024-08-13
Magnetic Resonance in Medicine (publié)
doi.org