Julien Cohen-Adad

A database of the healthy human spinal cord morphometry in the PAM50 template space

Sandrine Bédard

Miloš Keřkovský

Tomáš Rohan

Abstract Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of … (voir plus)spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher.

2024-02-01

Imaging Neuroscience (publié)

A database of the healthy human spinal cord morphometry in the PAM50 template space

Sandrine Bédard

Miloš Keřkovský

Tomáš Rohan

Abstract Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of … (voir plus)spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher.

2024-02-01

Imaging Neuroscience (publié)

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

Russell A. Poldrack

Christopher J. Markiewicz

Stefan Appelhoff

Yoni K. Ashar

Tibor Auer

Sylvain Baillet

Shashank Bansal

Leandro Beltrachini

Christian G. Benar

C. Bénar

Giacomo Bertazzoli

Suyash Bhogawar

Ross W. Blair

Marta Bortoletto

Mathieu Boudreau

Teon L. Brooks

Vince D. Calhoun

Filippo Maria Castelli

Patricia Clement

Alexander L. Cohen … (voir 100 de plus)

Sasha D’Ambrosio

Gilles de Hollander

María de la Iglesia-Vayá

Alejandro de la Vega

Arnaud Delorme

Orrin Devinsky

Dejan Draschkow

Eugene Paul Duff

E. Duff

Elizabeth DuPre

Eric Earl

Oscar Esteban

Franklin W. Feingold

Guillaume Flandin

Anthony Galassi

Giuseppe Gallitto

Melanie Ganz

Rémi Gau

James Gholam

Sulagna Dia Ghosh

Satrajit S. Ghosh

Alessio Giacomel

Ashley G. Gillman

Padraig Gleeson

Alexandre Gramfort

Samuel Guay

Giacomo Guidali

Yaroslav O. Halchenko

Daniel A. Handwerker

Nell Hardcastle

Peer Herholz

Dora Hermes

Christopher J. Honey

C. Honey

Robert B. Innis

Horea-Ioan Ioanas

Andrew Jahn

Agah Karakuzu

David B. Keator

Gregory Kiar

Balint Kincses

Angela R. Laird

Jonathan C. Lau

Alberto Lazari

Jon Haitz Legarreta

Adam Li

Xiangrui Li

Bradley C. Love

Hanzhang Lu

Eleonora Marcantoni

Camille Maumet

Giacomo Mazzamuto

Steven L. Meisler

Mark Mikkelsen

Henk Mutsaerts

Thomas E. Nichols

Aki Nikolaidis

Gustav Nilsonne

Guiomar Niso

Martin Norgaard

Thomas W. Okell

Robert Oostenveld

Eduard Ort

Patrick J. Park

Mateusz Pawlik

Cyril R. Pernet

Franco Pestilli

Jan Petr

Christophe Phillips

Jean-Baptiste Poline

Luca Pollonini

Pradeep R. Raamana

Pradeep Reddy Raamana

Petra Ritter

Gaia Rizzo

Kay A. Robbins

Alexander P. Rockhill

Christine Rogers

Ariel Rokem

Chris Rorden

Alexandre Routier

Jose Manuel Saborit-Torres

Taylor Salo

Michael Schirner

Robert E. Smith

Tamas Spisak

Julia Sprenger

Nicole C. Swann

Martin Szinte

Sylvain Takerkart

Bertrand Thirion

Adam G. Thomas

Sajjad Torabian

Gael Varoquaux

Bradley Voytek

Julius Welzel

Martin Wilson

Tal Yarkoni

Krzysztof J. Gorgolewski

2024-01-09

ArXiv (prépublication)

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 … (voir plus)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 (prépublication)

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 … (voir plus)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 (prépublication)

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 … (voir plus)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 (prépublication)

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 … (voir plus)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 (prépublication)

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

Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used qualitativ… (voir plus)e 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 three years of data collection and up to ten sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, FA, MD, and 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.

2024-01-01

Imaging Neuroscience (publié)

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

2024-01-01

Magnetic Resonance in Medical Sciences (publié)

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 (publié)

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

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 (

2023-10-23

ArXiv (prépublication)