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
Research Topics
Medical Machine Learning
Website
Google Scholar
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Biography

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

Rohan Banerjee
Collaborating Alumni - Polytechnique Montréal
Co-supervisor :
Gauthier Gidel
Website
Github
Google Scholar
Kahina Baouche
Research Intern - Polytechnique Montréal
Github
Pierre Louis Benveniste
PhD - Polytechnique Montréal
Co-supervisor :
Hervé Lombaert
Website
Github
Google Scholar
Armand Collin
PhD - Polytechnique Montréal
Github
Thomas Dagonneau
Master's Research - Polytechnique Montréal
Github
Naga Karthik Enamundram
PhD - Polytechnique Montréal
Co-supervisor :
Sarath Chandar
emvnagakarthik@gmail.com
Website
Github
Google Scholar
Paul Hoareau
Master's Research - Polytechnique Montréal
Github
Nilser Laines Medina
PhD - Polytechnique Montréal
Nathan Molinier
PhD - Polytechnique Montréal
Github
Kalum Ost
Collaborating researcher
Julien Thouveny
Master's Research - Université de Montréal
Arthur Toulouse
Master's Research - Polytechnique Montréal
Github
Jan Valosek
Postdoctorate - Polytechnique Montréal
Website
Github
Google Scholar

Publications

Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition.
Kurt G Schilling
Francesco Grussu
Andrada Ianus
Brian Hansen
Amy F. D. Howard
Rachel L. C. Barrett
Fatima Nasrallah
Manisha Aggarwal
Stijn Michielse
Warda Syeda
Nian Wang
Andrew F. Bagdasarian
Jelle Veraart
Alard Roebroeck
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Lucas Soustelle
Christien Bowman
Benjamin C. Tendler … (see 38 more)
Andreea Hertanu
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Mathieu D. Santin
Flavio Dell’Acqua
Carlo Pierpaoli
Samuel C. Grant
Ivana Drobnjak
Andre Obenaus
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Hao Huang
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Matthew D. Budde
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Ileana O. Jelescu
The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo d… (see more)MRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
2025-03-04
Magnetic Resonance in Medicine (published)
doi.org
arxiv.org
Considerations and recommendations from the <scp>ISMRM</scp> diffusion study group for preclinical diffusion <scp>MRI</scp>: Part 2—Ex vivo imaging: Added value and acquisition
Kurt G Schilling
Francesco Grussu
Andrada Ianus
Brian Hansen
Amy F. D. Howard
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 … (see 38 more)
Andreea Hertanu
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur 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
2025-03-04
Magnetic Resonance in Medicine (published)
doi.org
Normalizing Spinal Cord Compression Measures in Degenerative Cervical Myelopathy.
Sandrine Bédard
Jan Valosek
Maryam Seif
Armin Curt
Simon Schading-Sassenhausen
Nikolai Pfender
P. Freund
Markus Hupp
Julien Cohen-Adad
2025-03-01
The spine journal (published)
doi.org
Considerations and recommendations from the <scp>ISMRM</scp> diffusion study group for preclinical diffusion <scp>MRI</scp>: Part 1: In vivo small‐animal imaging
Ileana O. Jelescu
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
Andreea Hertanu … (see 37 more)
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur 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
Kurt G Schilling
2025-02-26
Magnetic Resonance in Medicine (published)
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 … (see 38 more)
Andreea Hertanu
Ben Jeurissen
Marleen Verhoye
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn
Karla Miller
Bennett Landman
Noam Shemesh
Arthur 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 (published)
doi.org
arxiv.org
Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small‐animal imaging
Ileana O. Jelescu
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
Ben Jeurissen
Lucio Frydman
Lucas Soustelle
Christien Bowman … (see 37 more)
Yohan van de Looij
Benjamin C. Tendler
David Hike
Jeff F. Dunn
Andreea Hertanu
Karla Miller
Bennett Landman
Marleen Verhoye
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Hao Huang
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Kurt G Schilling
Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffus… (see more)ion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
2025-02-26
Magnetic Resonance in Medicine (published)
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 Valosek
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… (see more) 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 (preprint)
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 Valosek
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… (see more) 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 (preprint)
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 … (see 1 more)
Julien Cohen-Adad
Purpose The depth within the body, small diameter, long length, and varying tissue surrounding the spinal cord impose specific consideration… (see more)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 (preprint)
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 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
Jürgen Finsterbusch
Katherine T. Martucci … (see 20 more)
Kimberly J. Hemmerling
Mahdi Mobarak-Abadi
Mark A. Hoggarth
Matthew A. Howard
Molly G. Bright
Nawal Kinany
O. Kowalczyk
Patrick Freund
Robert L. Barry
Sean Mackey
Shahabeddin Vahdat
Simon Schading
Stephen B McMahon
Todd Parish
Véronique Marchand-Pauvert
Yufen Chen
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… (see more)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 (preprint)
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
Christian W. Kündig
Christine S. W. Law
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 … (see 23 more)
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… (see more)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 (preprint)
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 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
Jürgen Finsterbusch
Katherine T. Martucci … (see 20 more)
Kimberly J. Hemmerling
Mahdi Mobarak-Abadi
Mark A. Hoggarth
Matthew A. Howard
Molly G. Bright
Nawal Kinany
O. Kowalczyk
Patrick Freund
Robert L. Barry
Sean Mackey
Shahabeddin Vahdat
Simon Schading
Stephen B McMahon
Todd Parish
Véronique Marchand-Pauvert
Yufen Chen
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… (see more)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 (preprint)
doi.org