Julien Cohen-Adad

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 Valošek

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 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

Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers

Mathieu Boudreau

Agah Karakuzu

Arnaud Boré

Basile Pinsard

Kiril Zelenkovski

Eva Alonso‐Ortiz

Julie Boyle

Lune Bellec

Julien Cohen-Adad

Abstract Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used q… (see more)ualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.

2025-01-01

Imaging Neuroscience (published)

doi.org

Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers

Mathieu Boudreau

Agah Karakuzu

Arnaud Boré

Basile Pinsard

Kiril Zelenkovski

Eva Alonso‐Ortiz

Julie Boyle

Lune Bellec

Julien Cohen-Adad

Abstract Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used q… (see more)ualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.

2025-01-01

Imaging Neuroscience (published)

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

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… (see more)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 (published)

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… (see more)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 (published)

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 (published)

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… (see more)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 (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

W. Syeda

Nian Wang

Jelle Veraart

Alard J. Roebroeck

Andrew F Bagdasarian

Cornelius Eichner

Farshid Sepehrband

Jan Zimmermann

L. Soustelle

Christien Bowman

Benjamin C. Tendler … (see 38 more)

A. Hertanu

Ben Jeurissen

Marleen Verhoye

Lucio Frydman

Y. Looij

David C. Hike

Jeff F. Dunn

Karla L. Miller

Bennett A. Landman

N. Shemesh

Adam Anderson

Emilie McKinnon

Shawna Farquharson

Flavio Dell’ Acqua

Carlo M. Pierpaoli

Ivana Drobnjak

Alexander Leemans

K. Harkins

Maxime Descoteaux

Duan Xu

Hao Huang

Mathieu D. Santin

Samuel C. Grant

Andre Obenaus

Gene S Kim

Dan Wu

D. Bihan

S. Blackband

Luisa Ciobanu

E. Fieremans

Ruiliang Bai

T. Leergaard

Jiangyang Zhang

T. Dyrby

G. A. Johnson

Julien Cohen-Adad

Matthew D. Budde

Ileana Ozana Jelescu

2024-10-24

ArXiv (preprint)

arxiv.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 (published)

doi.org

AI Insights for Policymakers

Mila Techaide 2025

The Development of the UN Scientific Panel on AI

Rising to the Occasion

Julien Cohen-Adad

Biography

Current Students

Publications

AI Insights for Policymakers

Mila Techaide 2025

The Development of the UN Scientific Panel on AI

Rising to the Occasion

Popular keywords:

Julien Cohen-Adad

Biography

Current Students

Publications