Jan Valosek

Andrew C. Smith

Dario Pfyffer

Simon Schading-Sassenhausen

Lynn Farner

Kenneth A. Weber

Patrick Freund

Background: Quantitative MRI biomarkers in spinal cord injury (SCI) can help understand the extent of the focal injury. However, due to the … (see more)lack of automatic segmentation methods, these biomarkers are derived manually, which is a time-consuming process prone to intra- and inter-rater variability, thus limiting large multi-site studies and translation to clinical workflows. Purpose: To develop a deep learning tool for the automatic segmentation of T2-weighted hyperintense lesions and the spinal cord in SCI patients. Material and Methods: This retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023 who underwent clinical MRI examination. A deep learning model, SCIseg, was trained on T2-weighted images with heterogeneous image resolutions (isotropic, anisotropic), and orientations (axial, sagittal) acquired using scanners from different manufacturers (Siemens, Philips, GE) and different field strengths (1T, 1.5T, 3T) for the automatic segmentation of SCI lesions and the spinal cord. The proposed method was visually and quantitatively compared with other open-source baseline methods. Quantitative biomarkers (lesion volume, lesion length, and maximal axial damage ratio) computed from manual ground-truth lesion masks and automatic SCIseg predictions were correlated with clinical scores (pinprick, light touch, and lower extremity motor scores). A between-group comparison was performed using the Wilcoxon signed-rank test. Results: MRI data from 191 SCI patients (mean age, 48.1 years {+/-} 17.9 [SD]; 142 males) were used for training. Compared to existing methods, SCIseg achieved the best segmentation performance for both the cord and lesions and generalized well to both traumatic and non-traumatic SCI patients. SCIseg is open-source and accessible through the Spinal Cord Toolbox. Conclusion: Automatic segmentation of intramedullary lesions commonly seen in traumatic SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model generalizes across lesion etiologies (traumatic, ischemic), scanner manufacturers and heterogeneous image resolutions.

2024-01-03

medRxiv (preprint)

SCIseg: Automatic Segmentation of T2-weighted Hyperintense Lesions in Spinal Cord Injury

Enamundram Naga Karthik

Andrew C. Smith

Dario Pfyffer

Simon Schading-Sassenhausen

Lynn Farner

Kenneth A. Weber

Patrick Freund

Background: Quantitative MRI biomarkers in spinal cord injury (SCI) can help understand the extent of the focal injury. However, due to the … (see more)lack of automatic segmentation methods, these biomarkers are derived manually, which is a time-consuming process prone to intra- and inter-rater variability, thus limiting large multi-site studies and translation to clinical workflows. Purpose: To develop a deep learning tool for the automatic segmentation of T2-weighted hyperintense lesions and the spinal cord in SCI patients. Material and Methods: This retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023 who underwent clinical MRI examination. A deep learning model, SCIseg, was trained on T2-weighted images with heterogeneous image resolutions (isotropic, anisotropic), and orientations (axial, sagittal) acquired using scanners from different manufacturers (Siemens, Philips, GE) and different field strengths (1T, 1.5T, 3T) for the automatic segmentation of SCI lesions and the spinal cord. The proposed method was visually and quantitatively compared with other open-source baseline methods. Quantitative biomarkers (lesion volume, lesion length, and maximal axial damage ratio) computed from manual ground-truth lesion masks and automatic SCIseg predictions were correlated with clinical scores (pinprick, light touch, and lower extremity motor scores). A between-group comparison was performed using the Wilcoxon signed-rank test. Results: MRI data from 191 SCI patients (mean age, 48.1 years {+/-} 17.9 [SD]; 142 males) were used for training. Compared to existing methods, SCIseg achieved the best segmentation performance for both the cord and lesions and generalized well to both traumatic and non-traumatic SCI patients. SCIseg is open-source and accessible through the Spinal Cord Toolbox. Conclusion: Automatic segmentation of intramedullary lesions commonly seen in traumatic SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model generalizes across lesion etiologies (traumatic, ischemic), scanner manufacturers and heterogeneous image resolutions.

2024-01-03

medRxiv (preprint)

SCIseg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury

Enamundram Naga Karthik

Andrew C. Smith

Dario Pfyffer

Simon Schading-Sassenhausen

Lynn Farner

Kenneth A. Weber

Patrick Freund

Background: Quantitative MRI biomarkers in spinal cord injury (SCI) can help understand the extent of the focal injury. However, due to the … (see more)lack of automatic segmentation methods, these biomarkers are derived manually, which is a time-consuming process prone to intra- and inter-rater variability, thus limiting large multi-site studies and translation to clinical workflows. Purpose: To develop a deep learning tool for the automatic segmentation of T2-weighted hyperintense lesions and the spinal cord in SCI patients. Material and Methods: This retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023 who underwent clinical MRI examination. A deep learning model, SCIseg, was trained on T2-weighted images with heterogeneous image resolutions (isotropic, anisotropic), and orientations (axial, sagittal) acquired using scanners from different manufacturers (Siemens, Philips, GE) and different field strengths (1T, 1.5T, 3T) for the automatic segmentation of SCI lesions and the spinal cord. The proposed method was visually and quantitatively compared with other open-source baseline methods. Quantitative biomarkers (lesion volume, lesion length, and maximal axial damage ratio) computed from manual ground-truth lesion masks and automatic SCIseg predictions were correlated with clinical scores (pinprick, light touch, and lower extremity motor scores). A between-group comparison was performed using the Wilcoxon signed-rank test. Results: MRI data from 191 SCI patients (mean age, 48.1 years {+/-} 17.9 [SD]; 142 males) were used for training. Compared to existing methods, SCIseg achieved the best segmentation performance for both the cord and lesions and generalized well to both traumatic and non-traumatic SCI patients. SCIseg is open-source and accessible through the Spinal Cord Toolbox. Conclusion: Automatic segmentation of intramedullary lesions commonly seen in traumatic SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model generalizes across lesion etiologies (traumatic, ischemic), scanner manufacturers and heterogeneous image resolutions.

2024-01-03

medRxiv (preprint)

Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox.

2024-01-01

Magnetic Resonance in Medical Sciences (published)

SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury

Enamundram Naga Karthik

Lynn Farner

Dario Pfyffer

Simon Schading-Sassenhausen

Anna Lebret

Gergely David

Andrew C. Smith

Kenneth A. Weber

Maryam Seif

Rhscir Network Imaging Group

Patrick Freund

2024-01-01

AMAI@MICCAI (published)

arxiv.org

Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

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

Rebecca S. Samson

Francesco Grussu

Marco Battiston

Claudia A. M. Gandini Wheeler-Kingshott

Marios C. Yiannakas … (see 4 more)

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… (see more) 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)

Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

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

Rebecca S. Samson

Francesco Grussu

Marco Battiston

Claudia A. M. Gandini Wheeler-Kingshott

Marios C. Yiannakas … (see 4 more)

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… (see more) 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)

Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

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

Rebecca Sara Samson

Francesco Grussu

Marco Battiston

C. G. Gandini Wheeler-Kingshott

Marios C. Yiannakas … (see 4 more)

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… (see more) 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)

Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Eva Alonso‐Ortiz

Mihael Abramovic

Carina Arneitz

Nicole Atcheson

Laura Barlow

Robert L. Barry

Markus Barth

Marco Battiston

Christian Büchel

Matthew D. Budde

Virginie Callot

Anna J. E. Combes

Benjamin De Leener

Maxime Descoteaux

Paulo Loureiro de Sousa

Marek Dostál

Julien Doyon

Adam Dvorak

Falk Eippert … (see 71 more)

Karla R. Epperson

Kevin S. Epperson

Patrick Freund

Jürgen Finsterbusch

Alexandru Foias

Michela Fratini

Issei Fukunaga

Claudia A. M. Gandini Wheeler-Kingshott

Giancarlo Germani

Guillaume Gilbert

Federico Giove

Charley Gros

Francesco Grussu

Akifumi Hagiwara

Pierre-Gilles Henry

Tomáš Horák

Masaaki Hori

James Joers

Kouhei Kamiya

Haleh Karbasforoushan

Miloš Keřkovský

Ali Khatibi

Joo‐Won Kim

Nawal Kinany

Hagen H. Kitzler

Shannon Kolind

Yazhuo Kong

Petr Kudlička

Paul Kuntke

Nyoman D. Kurniawan

Slawomir Kusmia

René Labounek

Maria Marcella Lagana

Cornelia Laule

Christine S. Law

Christophe Lenglet

Tobias Leutritz

Yaou Liu

Sara Llufriu

Sean Mackey

Eloy Martinez-Heras

Loan Mattera

Igor Nestrašil

Kristin P. O’Grady

Nico Papinutto

Daniel Papp

Deborah Pareto

Todd B. Parrish

Anna Pichiecchio

Ferran Prados

Àlex Rovira

Marc J. Ruitenberg

Rebecca S. Samson

Giovanni Savini

Maryam Seif

Alan C. Seifert

Alex K. Smith

Seth A. Smith

Zachary A. Smith

Elisabeth Solana

Y. Suzuki

George Tackley

Alexandra Tinnermann

Dimitri Van De Ville

Marios C. Yiannakas

Kenneth A. Weber

Nikolaus Weiskopf

Richard G. Wise

Patrik O. Wyss

Junqian Xu

2021-09-23

Scientific Data (published)

Generic acquisition protocol for quantitative MRI of the spinal cord

Eva Alonso‐Ortiz

Mihael Abramovic

Carina Arneitz

Nicole Atcheson

Laura Barlow

Robert L. Barry

Markus Barth

Marco Battiston

Christian Büchel

Matthew D. Budde

Virginie Callot

Anna J. E. Combes

Benjamin De Leener

Maxime Descoteaux

Paulo Loureiro de Sousa

Marek Dostál

Julien Doyon

Adam Dvorak

Falk Eippert … (see 71 more)

Karla R. Epperson

Kevin S. Epperson

Patrick Freund

Jürgen Finsterbusch

Alexandru Foias

Michela Fratini

Issei Fukunaga

Claudia A. M. Gandini Wheeler-Kingshott

Giancarlo Germani

Guillaume Gilbert

Federico Giove

Charley Gros

Francesco Grussu

Akifumi Hagiwara

Pierre-Gilles Henry

Tomáš Horák

Masaaki Hori

James Joers

Kouhei Kamiya

Haleh Karbasforoushan

Miloš Keřkovský

Ali Khatibi

Joo‐Won Kim

Nawal Kinany

Hagen H. Kitzler

Shannon Kolind

Yazhuo Kong

Petr Kudlička

Paul Kuntke

Nyoman D. Kurniawan

Slawomir Kusmia

René Labounek

Maria Marcella Lagana

Cornelia Laule

Christine S. Law

Christophe Lenglet

Tobias Leutritz

Yaou Liu

Sara Llufriu

Sean Mackey

Eloy Martinez-Heras

Loan Mattera

Igor Nestrašil

Kristin P. O’Grady

Nico Papinutto

Daniel Papp

Deborah Pareto

Todd B. Parrish

Anna Pichiecchio

Ferran Prados

Àlex Rovira

Marc J. Ruitenberg

Rebecca S. Samson

Giovanni Savini

Maryam Seif

Alan C. Seifert

Alex K. Smith

Seth A. Smith

Zachary A. Smith

Elisabeth Solana

Yuichi Suzuki

George Tackley

Alexandra Tinnermann

Dimitri Van De Ville

Marios C. Yiannakas

K. Weber

Nikolaus Weiskopf

Richard G. Wise

Patrik O. Wyss

Junqian Xu

2021-08-16

Nature Protocols (published)

Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Eva Alonso‐Ortiz

Mihael Abramovic

Carina Arneitz

Nicole Atcheson

Laura Barlow

Robert L. Barry

Markus Barth

Marco Battiston

Christian Büchel

Matthew D. Budde

Virginie Callot

Anna J. E. Combes

Benjamin De Leener

Maxime Descoteaux

Paulo Loureiro de Sousa

Marek Dostál

Julien Doyon

Adam Dvorak

Falk Eippert … (see 71 more)

Karla R. Epperson

Kevin S. Epperson

Patrick Freund

Jürgen Finsterbusch

Alexandru Foias

Michela Fratini

Issei Fukunaga

Claudia A. M. Gandini Wheeler-Kingshott

Giancarlo Germani

Guillaume Gilbert

Federico Giove

Charley Gros

Francesco Grussu

Akifumi Hagiwara

Pierre-Gilles Henry

Tomáš Horák

Masaaki Hori

James Joers

Kouhei Kamiya

Haleh Karbasforoushan

Miloš Keřkovský

Ali Khatibi

Joo‐Won Kim

Nawal Kinany

Hagen H. Kitzler

Shannon Kolind

Yazhuo Kong

Petr Kudlička

Paul Kuntke

Nyoman D. Kurniawan

Slawomir Kusmia

René Labounek

Maria Marcella Lagana

Cornelia Laule

Christine S. Law

Christophe Lenglet

Tobias Leutritz

Yaou Liu

Sara Llufriu

Sean Mackey

Eloy Martinez-Heras

Loan Mattera

Igor Nestrašil

Kristin P. O’Grady

Nico Papinutto

Daniel Papp

Deborah Pareto

Todd B. Parrish

Anna Pichiecchio

Ferran Prados

Àlex Rovira

Marc J. Ruitenberg

Rebecca S. Samson

Giovanni Savini

Maryam Seif

Alan C. Seifert

Alex K. Smith

Seth A. Smith

Zachary A. Smith

Elisabeth Solana

Y. Suzuki

George Tackley

Alexandra Tinnermann

Dimitri Van De Ville

Marios C. Yiannakas

Kenneth A. Weber

Nikolaus Weiskopf

Richard G. Wise

Patrik O. Wyss

Junqian Xu

2021-08-16

Scientific Data (published)

Diffusion magnetic resonance imaging reveals tract‐specific microstructural correlates of electrophysiological impairments in non‐myelopathic and myelopathic spinal cord compression

René Labounek

Tomáš Horák

Magda Horáková

Petr Bednařík

Miloš Keřkovský

Jan Kočica

Tomáš Rohan

Christophe Lenglet

Petr Hluštík

Eva Vlčková

Zdeněk Kadaňka

Josef Bednařík

Alena Svatkova

Non‐myelopathic degenerative cervical spinal cord compression (NMDC) frequently occurs throughout aging and may progress to potentially ir… (see more)reversible degenerative cervical myelopathy (DCM). Whereas standard clinical magnetic resonance imaging (MRI) and electrophysiological measures assess compression severity and neurological dysfunction, respectively, underlying microstructural deficits still have to be established in NMDC and DCM patients. The study aims to establish tract‐specific diffusion MRI markers of electrophysiological deficits to predict the progression of asymptomatic NMDC to symptomatic DCM.

2021-07-21

European Journal of Neurology (published)