Sandrine Bédard

Kalum Ost

Maxime Bouthillier

Gergely Dávid

Maryam Seif

Armin Curt

Nikolai Pfender

Markus Hupp

Patrick Freund

Tomáš Horák

Petr Kudlička

Josef Bednařík

Fauziyya Muhammad

Zachary A. Smith

2026-01-31

European Journal of Radiology Artificial Intelligence (published)

Monitoring morphometric drift in lifelong learning segmentation of the spinal cord.

Enamundram Naga Karthik

Christoph Stefan Aigner

Elise Bannier

Josef Bednařík

Virginie Callot

Anna Combes

Armin Curt

Gergely Dávid

Falk Eippert

Lynn Farner

Michael G. Fehlings

Patrick Freund

Tobias Granberg

Cristina Granziera

RHSCIR Network Imaging Group

Ulrike Horn

Tomáš Horák

Suzanne Humphreys … (see 36 more)

Markus Hupp

Anne Kerbrat

Nawal Kinany

Shannon Kolind

Petr Kudlička

Anna Lebret

Lisa Eunyoung Lee

Cristina Granziera

Allan R. Martin

Govind Nair

Megan McGrath

Kristin P. O’Grady

Jiwon Oh

Russell Ouellette

Nikolai Pfender

Dario Pfyffer

Pierre‐François Pradat

Alexandre Prat

Daniel S. Reich

Ilaria Ricchi

Naama Rotem‐Kohavi

Simon Schading-Sassenhausen

Maryam Seif

Andrew Smith

Seth A. Smith

Grace Sweeney

Roger Tam

Anthony Traboulsee

Constantina A. Treaba

Charidimos Tsagkas

Dimitri Van De Ville

Zachary Vavasour

Kenneth A. Weber

Sarath Chandar

Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and … (see more)injuries affecting the spinal cord. For instance, the spinal cord cross-sectional area can be used to monitor cord atrophy in multiple sclerosis and to characterize compression in degenerative cervical myelopathy. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite (n=75) dataset, including 9 different MRI contrasts and several spinal cord pathologies. We also introduce a lifelong learning framework to automatically monitor the morphometric drift as the model is updated using additional datasets. The framework is triggered by an automatic GitHub Actions workflow every time a new model is created, recording the morphometric values derived from the model's predictions over time. As a real-world application of the proposed framework, we employed the spinal cord segmentation model to update a recently-introduced normative database of healthy participants containing commonly used measures of spinal cord morphometry. Results showed that: (i) our model performs well compared to its previous versions and existing pathology-specific models on the lumbar spinal cord, images with severe compression, and in the presence of intramedullary lesions and/or atrophy achieving an average Dice score of 0.95 ± 0.03; (ii) the automatic workflow for monitoring morphometric drift provides a quick feedback loop for developing future segmentation models; and (iii) the scaling factor required to update the database of morphometric measures is nearly constant among slices across the given vertebral levels, showing minimum drift between the current and previous versions of the model monitored by the framework. The model is freely available in Spinal Cord Toolbox v7.0.

2026-01-21

Imaging neuroscience (published)

openreview.net

RootletSeg: Deep learning method for spinal rootlets segmentation across MRI contrasts

Katerina Krejci

Jiri Chmelik

Sandrine B'edard

Falk Eippert

Ulrike Horn

Virginie Callot

Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Method… (see more)s: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed to segment C2-T1 dorsal and ventral spinal rootlets. Training was performed on 76 scans and testing on 17 scans. The Dice score was used to compare the model performance with an existing open-source method. Spinal levels derived from RootletSeg segmentations were compared with vertebral levels defined by intervertebral discs using Bland-Altman analysis. Results: The RootletSeg model developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years

2025-09-17

ArXiv (preprint)

RootletSeg: Deep learning method for spinal rootlets segmentation across MRI contrasts

Katerina Krejci

Jiri Chmelik

Sandrine B'edard

Falk Eippert

Ulrike Horn

Virginie Callot

Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Method… (see more)s: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed to segment C2-T1 dorsal and ventral spinal rootlets. Training was performed on 76 scans and testing on 17 scans. The Dice score was used to compare the model performance with an existing open-source method. Spinal levels derived from RootletSeg segmentations were compared with vertebral levels defined by intervertebral discs using Bland-Altman analysis. Results: The RootletSeg model developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years

2025-09-17

ArXiv (preprint)

Rootlets-based registration to the PAM50 spinal cord template

Valeria Oliva

Kenneth A. Weber

Abstract Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxel-wise group analyses. Traditiona… (see more)l template-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n = 267, 44 sites) and a multi-subject dataset with various neck positions (n = 10, 3 sessions). We further validated the method on task-based functional MRI (n = 23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared with the traditional disc-based method on n = 226 individuals, and on n = 176 individuals for morphological analyses. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based registration increased Z scores and activation cluster size compared with disc-based registration (number of active voxels from 3292 to 7978). Rootlet-based registration enhances both inter- and intra-subject anatomical alignment and yields better spatial normalization for group-level fMRI analyses. Our findings highlight the potential of rootlet-based registration to improve the precision and reliability of spinal cord neuroimaging group analysis.

2025-08-26

Imaging Neuroscience (published)

Monitoring morphometric drift in lifelong learning segmentation of the spinal cord

Enamundram Naga Karthik

Christoph S. Aigner

Elise Bannier

Josef Bednařík

Virginie Callot

Anna Combes

Armin Curt

Gergely Dávid

Falk Eippert

Lynn Farner

Michael G Fehlings

Patrick Freund

Tobias Granberg

Cristina Granziera

RHSCIR Network Imaging Group

Ulrike Horn

Tomáš Horák

Suzanne Humphreys … (see 36 more)

Markus Hupp

Anne Kerbrat

Nawal Kinany

Shannon Kolind

Petr Kudlička

Anna Lebret

Lisa Eunyoung Lee

Caterina Mainero

Allan R. Martin

Megan McGrath

Govind Nair

Kristin P. O’Grady

Jiwon Oh

Russell Ouellette

Nikolai Pfender

Dario Pfyffer

P. Pradat

Alexandre Prat

Emanuele Pravatà

Daniel S. Reich

Ilaria Ricchi

Naama Rotem-Kohavi

Simon Schading-Sassenhausen

Maryam Seif

Andrew C. Smith

Seth Aaron Smith

Grace Sweeney

Roger Tam

Anthony Traboulsee

Constantina A. Treaba

Charidimos Tsagkas

Zachary Vavasour

Dimitri Van De Ville

Kenneth A. Weber

Sarath Chandar

2025-06-12

lifelong-ml.cc/CoLLAs/2025/Workshop_Track (published)

openreview.net

Monitoring morphometric drift in lifelong learning segmentation of the spinal cord

Enamundram Naga Karthik

Sandrine B'edard

Jan Valovsek

Christoph Aigner

Elise Bannier

Josef Bednavr'ik

Virginie Callot

Anna Combes

Armin Curt

Gergely Dávid

Falk Eippert

Lynn Farner

M. G. Fehlings

Patrick Freund

Tobias Granberg

Cristina Granziera

RHSCIR Network Imaging Group

Ulrike Horn

Tom'avs Hor'ak

Suzanne Humphreys … (see 36 more)

Markus Hupp

Anne Kerbrat

Nawal Kinany

Shannon Kolind

Petr Kudlivcka

Anna Lebret

Lisa Eunyoung Lee

Caterina Mainero

Allan R. Martin

Megan McGrath

Govind Nair

Kristin P. O’Grady

Jiwon Oh

Russell Ouellette

Nikolai Pfender

Dario Pfyffer

P. Pradat

Alexandre Prat

Emanuele Pravatà

D. S. Reich

Ilaria Ricchi

Naama Rotem-Kohavi

Simon Schading-Sassenhausen

Maryam Seif

Andrew C. Smith

Seth Aaron Smith

Grace Sweeney

Roger Tam

Anthony Traboulsee

Constantina A. Treaba

Charidimos Tsagkas

Zachary Vavasour

Dimitri Van De Ville

Kenneth A. Weber

Sarath Chandar

2025-05-02

ArXiv (preprint)

Rootlets-based registration to the spinal cord PAM50 template

Valeria Oliva

Kenneth A. Weber

2025-05-01

arXiv (published)

Normalizing Spinal Cord Compression Measures in Degenerative Cervical Myelopathy.

Maryam Seif

Armin Curt

Simon Schading-Sassenhausen

Nikolai Pfender

P. Freund

Markus Hupp

2025-03-01

The spine journal (published)

Body size and intracranial volume interact with the structure of the central nervous system: A multi-center in vivo neuroimaging study

René Labounek

Monica T. Bondy

Amy L. Paulson

Mihael Abramovic

Eva Alonso‐Ortiz

Nicole Atcheson

Laura R. Barlow

Robert L. Barry

Markus Barth

Marco Battiston

Christian Büchel

Matthew D. Budde

Virginie Callot

Anna Combes

Benjamin De Leener

Maxime Descoteaux

Paulo Loureiro de Sousa

Marek Dostál

Julien Doyon … (see 74 more)

Adam Dvorak

Falk Eippert

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

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

Maria Marcella Lagana

Cornelia Laule

Christine S. W. Law

Csw Law

Tobias Leutritz

Yaou Liu

Sara Llufriu

Sean Mackey

Allan R. Martin

Eloy Martinez-Heras

Loan Mattera

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 Aaron Smith

Zachary A. Smith

Elisabeth Solana

Yuichi 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

Christophe Lenglet

Igor Nestrašil

2025-01-01

Imaging Neuroscience (published)

Towards contrast-agnostic soft segmentation of the spinal cord

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 … (see more)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. (published)

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

René Labounek

Monica T. Bondy

Amy L. Paulson

Mihael Abramovic

Eva Alonso‐Ortiz

Nicole Atcheson

Laura R. Barlow

Robert L. Barry

Markus Barth

Marco Battiston

Christian Büchel

Matthew D. Budde

Virginie Callot

Anna Combes

Benjamin De Leener

Maxime Descoteaux

Paulo Loureiro de Sousa

Marek Dostál

Julien Doyon … (see 73 more)

Adam Dvorak

Falk Eippert

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

Francesco Grussu

Akifumi Hagiwara

Pierre-Gilles Henry

Tomáš Horák

Masaaki Hori

James M. 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

Maria Marcella Lagana

Cornelia Laule

Csw Law

Tobias Leutritz

Yaou Liu

Sara Llufriu

Sean Mackey

Allan R. Martin

Eloy Martinez-Heras

Loan Mattera

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

Kenneth A. Weber

Nikolaus Weiskopf

Richard G. Wise

Patrik O. Wyss

Junqian Xu

Christophe Lenglet

Igor Nestrašil

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controllin… (see more)g for sources of biological variation such as subject’s sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

2024-05-01

bioRxiv (preprint)