Julien Cohen-Adad

Thomas Dagonneau

Maîtrise recherche - Polytechnique

Naga Karthik Enamundram

Doctorat - Polytechnique

Co-superviseur⋅e :

Maîtrise recherche - Polytechnique

Youssef Laatar

Maîtrise recherche - Polytechnique

Julien Laborde-Peyré

Stagiaire de recherche - Polytechnique

Nilser Laines Medina

Doctorat - Polytechnique

Nathan Molinier

Doctorat - Polytechnique

Quentin Revillon

Stagiaire de recherche - Polytechnique

Arthur Toulouse

Maîtrise recherche - Polytechnique

Publications

Charting Cervical Spinal Cord Morphometry Across the Lifespan

Kurt Schilling

Michael E Kim

Matthew Amandola

Chenyu Gao

Karthik Ramadass

Praitayini Kanakaraj

Sam Bogdanov

G Rudravaram

Nancy R. Newlin

Derek B. Archer

Timothy J Hohman

Angela L Jefferson

Victoria L Morgan

Alexandra Roche

Dario J Englot

Murat Bilgel

Lori L Beason-Held

Luigi Ferrucci

Laurie Cutting

Laura A Barquero … (voir 21 de plus)

Micah D’Archangel

Tin Q Nguyen

Kathryn L Humphreys

Yanbin Niu

Sophia Vinci-Booher

Carissa J. Cascio

Zhiyuan Li

Daniel Moyer

Simon Vandekar

Panpan Zhang

Samuelle St-Onge

Benjamin De Leener

John C Gore

Seth Smith

B A Landman

John C. Gore

Seth Smith

Bennett A. Landman

Abstract Spinal cord morphometry provides essential biomarkers of neurological health, but clinical interpretations are confounded by inter-… (voir plus)subject variability and a lack of normative references across the full human lifespan. We address this gap by generating the first comprehensive lifespan charts for cervical spinal cord morphometry. We leveraged 30 population-based brain MRI datasets, aggregating 78,269 scans from 41,042 individuals (ages 0–100) whose imaging protocols included cervical cord coverage. To overcome contrast variability, we employed a state-of-the-art contrast-agnostic deep learning segmentation method, extracting cross-sectional area (CSA), anteroposterior (AP) and right–left (RL/transverse), and shape indices (compression ratio, eccentricity, and solidity) from C1 to C7. Normative trajectories were modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). The resulting charts reveal distinct non-linear lifespan changes: rapid growth through childhood and adolescence, peak maturation occurring in early-to-mid adulthood (e.g., mid-30s for CSA), followed by gradual decreases. Significant regional variations along the cervical cord and consistent sex differences (males > females for size metrics) were quantified. Spinal cord trajectories showed strong temporal coupling with brain white matter and brainstem volumes, suggesting integrated CNS development and aging. These lifespan charts provide a robust normative framework, enabling age- and sex-specific centile scoring of individual spinal cord morphometry. This resource offers a critical tool for differentiating typical variation from pathological changes, enhancing the clinical utility of spinal cord MRI in studies of development and neurodegeneration.

2026-06-07

bioRxiv (accepté)

Automated robust segmentation of the spinal canal on MRI

Abel Salmona

Kalum Ost

Maxime Bouthillier

Gergely David

Maryam Seif

Armin Curt

Nikolai Pfender

Markus Hupp

Patrick Freund

Tomáš Horák

Petr Kudlička

Josef Bednařík

Fauziyya Muhammad

Zachary A. Smith

2026-05-31

European Journal of Radiology Artificial Intelligence (publié)

Spinal cord imaging for multiple sclerosis: Advances, priorities, and opportunities

Cornelia Laule

Atlee A Witt

Gabriele C De Luca

Cristina Granziera

B Mark Keegan

Anne Kerbrat

Eric C Klawiter

Shannon Kolind

Kristin P O’Grady

Jiwon Oh

Kurt G Schilling

Dinesh K Sivakolundu

Seth A Smith

Ceren Tozlu

Irene M Vavasour

Francesca Bagnato

Susan A Gauthier

Caterina Mainero

Eva Alonso-Ortiz … (voir 9 de plus)

Rohit Bakshi

Erin S Beck

Matthew R Brier

Christopher C Hemond

Stephen Krieger

David KB Li

Russell T Shinohara

Roland G Henry

North American Imaging in Multiple Sclerosis (NAIMS) Cooperative

The spinal cord plays a central role in the pathophysiology and clinical manifestations of multiple sclerosis (MS), yet remains under-studie… (voir plus)d compared with the brain. This review summarizes key insights from the 2025 North American Imaging in MS Spinal Cord Imaging Workshop, highlighting recent advances, ongoing challenges, and future opportunities in MS spinal cord imaging. We review pathological studies and outline the clinical relevance of spinal cord lesions and atrophy for diagnosis, prognosis, and disease monitoring, highlighting emerging biomarkers of progression independent of relapse activity. Correlations between magnetic resonance imaging, histopathology, and clinical outcomes support the validation and translational potential of advanced spinal cord imaging techniques. Finally, we discuss spinal cord–specific processing pipelines and reproducibility challenges. Collectively, these insights underscore the need to integrate advanced and quantitative spinal cord imaging into clinical trials, research studies, and—when feasible—clinical care, to fully capture the extent of MS pathology, and ultimately improve patient outcomes.

2026-05-31

Multiple Sclerosis Journal (publié)

Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data

Paul Hoareau

Kuan Yi Wang

Brandon Bujak

Roy Sun

Govind Nair

Irene Cortese

Charidimos Tsagkas

Daniel Reich

INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, … (voir plus)forcing reliance on sparse 2D slices. Weakly supervised Sparse-to-Dense frameworks bridge this gap, but guidelines remain ambiguous regarding human-centric visual enhancements and transferring optimization strategies across dimensions. We analyze divergent regularization needs for multi-class segmentation of high-resolution ex vivo spinal cord MRI. METHODS | We used 9.4T MRI of multiple sclerosis spinal cords (>104,000 slices) with sparse annotations (428 slices). A 2D Teacher trained on sparse slices generated dense pseudo-labels to train a 3D Student. We systematically evaluated the impact of human-centric preprocessing, spatial augmentation, and soft-label regularization on both architectures. RESULTS | We identified a critical divergence in training dynamics. The 2D Teacher required strong spatial augmentation and soft-labeling to overcome data scarcity, improving White Matter Lesion Dice scores by>11 points. However, propagating these techniques to the 3D Student degraded its performance. Furthermore, human-centric preprocessing (e.g., CLAHE) disrupted global statistical cues, dropping Gray Matter Lesion Dice scores by ~25 points. DISCUSSION | Our study highlights a perception divergence (human-centric contrast enhancement harms machine models) and a regularization conflict across dimensions. 3D architectures trained on dense pseudo-labels exhibit fundamentally different optimization landscapes than 2D counterparts and require distinct, conservative regularization. Code and models: https://github.com/ivadomed/model_seg_sc-gm-lesion_human_ms_exvivo_t2star.

2026-05-11

arXiv (prépublication)

arxiv.org

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation

Nathan Molinier

Hendrik Möller

Thomas Dagonneau

Anna Curto-Vilalta

Robert Graf

Matan Atad

Daniel Rueckert

Jan S. Kirschke

Deep learning-based medical image segmentation is increasingly used to support clinical diagnosis and develop new treatment strategies. Howe… (voir plus)ver, model performance remains limited by the scarcity of high-quality annotated data and insufficient generalization across imaging protocols. This limitation is particularly evident in MRI and CT, where models are typically trained on a single acquisition sequence and exhibit reduced robustness when applied to unseen sequences or contrasts. Although data augmentation is widely used to improve general robustness on medical images, its impact on cross-modality generalization has not been quantitatively explored. In this work, we study a targeted set of data augmentation techniques designed to improve cross-modality transfer. We train three spine segmentation models, each on a single-modality/sequence dataset, and evaluate them across seven out-of-distribution datasets (spanning CT and MRI), reflecting a realistic single-sequence training and multi-sequence/contrast/modality deployment scenario. Our results demonstrate substantial performance gains on unseen domains (average Dice gain of 155 %) while preserving in-domain accuracy (average Dice decrease of 0.008 %), including effective transfer between CT and MRI. To mitigate the computational cost typically associated with strong data augmentation, we implement GPU-optimized augmentations that maintain, and even improve, training efficiency by approximately 10 %. We release our approach as an open-source toolbox, enabling seamless integration into commonly used frameworks such as nnUNet and MONAI. These augmentations significantly enhance robustness to heterogeneous clinical imaging scenarios without compromising training speed.

2026-05-03

arXiv (prépublication)

arxiv.org

Segmentation of spinal rootlets across MRI contrasts with RootletSeg.

Katerina Krejci

Jiri Chmelik

Falk Eippert

Ulrike Horn

Virginie Callot

Segmentation of spinal nerve rootlets is relevant for spinal level estimation, lesion classification, neuromodulation therapy, and group-lev… (voir plus)el analyses. The aim of this study was to develop a deep learning method for the automatic segmentation of C2-T1 dorsal and ventral spinal nerve rootlets on various MRI scans. The study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T turbo spin echo T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years ± 6.53 [SD]; 28 [56%] males, 22 [44%] females) and achieved a mean ± SD Dice score of 0.67 ± 0.09 for T1w-INV2, 0.65 ± 0.11 for UNIT1, 0.64 ± 0.08 for T2w, and 0.62 ± 0.10 for T1w-INV1 contrasts. RootletSeg accurately segmented C2-T1 spinal rootlets across MRI contrasts, enabling the determination of spinal levels directly from MRI scans. The method is open-source and can be used for a variety of downstream analyses.

2026-05-01

Scientific Reports (publié)

Automatic multiple sclerosis lesion segmentation in the spinal cord using 3 T and 7 T MP2RAGE images

Nilser Laines Medina

Samira Mchinda

Benoit Testud

Sarah Demortière

Emanuele Pravatà

Govind Nair

Daniel S. Reich

Cristina Granziera

Charidimos Tsagkas

Virginie Callot

2026-04-30

Multiple Sclerosis and Related Disorders (publié)

Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers

Pierre‐Louis Benveniste

Laurent Létourneau‐Guillon

David Araújo

Lydia Chougar

Dumitru Fetco

Masaaki Hori

Kouhei Kamiya

Steven Messina

Charidimos Tsagkas

Bertrand Audoin

Rohit Bakshi

Élise Bannier

Daniel Blezek

Jean‐Christophe Brisset

Virginie Callot

Erik Charlson

Michelle Chen

Olga Ciccarelli

Sarah Demortière

Gilles Edan … (voir 36 de plus)

M Filippi

Tobias Granberg

Cristina Granziera

Christopher C. Hemond

B. Mark Keegan

Anne Kerbrat

J Kirschke

Petr Kudlička

Pierre Labauge

Lisa Eunyoung Lee

Yaou Liu

Caterina Mainero

Julian McGinnis

Nilser Laines Medina

Mark Mühlau

Govind Nair

Kristin P. O’Grady

Jiwon Oh

Russell Ouellette

Alexandre Prat

Daniel S. Reich

Maria A. Rocca

Timothy M. Shepherd

Seth A. Smith

Leszek Stawiarz

Jason Talbott

Roger Tam

Shahamat Tauhid

Anthony Traboulsee

Constantina A. Treaba

Paola Valsasina

Zachary Vavasour

Marios Yiannakas

Hervé Lombaert

Shannon Kolind

The proposed model can achieve accurate and reliable spinal cord MS lesion segmentation across heterogeneous MRI data, addressing a key barr… (voir plus)ier to clinical translation. The model is available in the Spinal Cord Toolbox v7.2 and higher.Code repository: https://github.com/ivadomed/seg-sc-ms-lesion-multicontrast.

2026-04-23

Multiple Sclerosis Journal (publié)

White and Gray Matter Multiple Sclerosis Spinal Cord Lesion Characteristics and Individualized Tissue Damage Assessment Using 7 T T1 Mapping

Nilser Laines-Medina

Samira Mchinda

Benoit Testud

Arnaud Le Troter

Lauriane Pini

Bertrand Audoin

Jean Pelletier

Sarah Demortière

Virginie Callot

The aim of this exploratory study was to demonstrate how 7 T MP2RAGE T1 mapping can be used to evaluate spinal cord (SC) tissue damage and l… (voir plus)esion characteristics in multiple sclerosis (MS) at both subregional and individual levels. Fifteen patients with relapsing-remitting MS (pwRRMS; mean disease duration = 32 ± 24.9 mo) and 15 age-matched healthy controls (HC) underwent 7 T cervical 3D MP2RAGE imaging with submillimetric spatial resolution. Automatic SC and lesion segmentations were obtained and manually corrected when necessary. Images were registered to the AMU7T template space to extract T1 values from specific regions of interest (ROIs), including white matter (WM) tracts: corticospinal (CST), lateral sensory (LST), posterior sensory (PST), ventral motor (VMT), and gray matter (GM) subregions: ventral, intermediate, and dorsal. Individual Z -score maps were computed and used to derive a global index of tissue impairment (patient-specific Z -score barplot) for lesion and normal appearing tissues (NAT). Finally, MS lesions were further characterized by their relative lesion load (RLL%), frequency maps, and topography across ROIs. Lesions were predominantly located in the posterior half of the cord, with GM showing the highest RLL. However, no lesions were observed exclusively in GM. An increasing gradient in T1 values was observed, with T1_HC 0.01). Mixed GM-WM lesions exhibited higher T1 values and larger volumes than WM-only lesions. Elevated T1 values

2026-04-12

Investigative Radiology (publié)

Spatial distribution of spinal cord fMRI activity with electrocutaneous stimulation

Merve Kaptan

Teresa Indriolo

Christine SW Law

Dario Pfyffer

Lindsay Lee

John K Ratliff

Serena S. Hu

Suzanne Tharin

Zachary A. Smith

GARY GLOVER

Sean C Mackey

Kenneth A. Weber

Sensory organization at the spinal segment level is commonly inferred from dermatomal maps that assume a fixed correspondence between cutane… (voir plus)ous regions and spinal segments. However, based on the complexities of spinal neuroanatomy and neurophysiology, the distribution of sensory signals within the cord may be broader and less segment-specific than dermatomal maps suggest, leaving the segment-level localization of sensory-evoked activity in humans uncertain. Spinal cord functional magnetic resonance imaging (fMRI) is currently the only technique capable of noninvasively mapping sensory activity with high spatial resolution in the human spinal cord. However, its application remains technically challenging and is limited by the uncertainty in segmental localization. In this study, we leveraged recent advancements in spinal cord fMRI, including spinal nerve rootlet-based spatial normalization, to investigate how sensory information is represented and distributed within the human spinal cord during electrocutaneous stimulation of the third digit of the right hand (i.e., C7 dermatome). Forty healthy adults were scanned with electrocutaneous stimulation at four individualized intensities across multiple runs to quantify (i) the rostrocaudal distribution of sensory-evoked activity, (ii) intensity-dependent changes in detectability and localization, and (iii) the effect of normalization strategy on segmental localization. Across participants, stimulation produced activation localized in the lower cervical cord (e.g., C6-C8), with the most consistent segmental localization near C7. Stronger stimulation increased detectability and produced more consistent segmental localization across participants. Importantly, normalization that incorporated nerve rootlet landmarks sharpened localization and improved sensitivity relative to conventional intervertebral disc-based alignment. This highlights the value of functionally relevant anatomical landmarks for group inference in the spinal cord. Responses were strongest in the initial run and attenuated with repetition, suggesting habituation or adaptation that can bias multi-run paradigms if unmodeled. Together, our results define practical acquisition and analysis conditions (e.g., stimulation strength, anatomical alignment strategy, and run structure) under which segment-level spinal sensory responses can be detected, thereby supporting more reliable studies of human spinal cord future basic and translational studies, including pain mechanisms, sensory function, and spinal injury.

2026-03-01

medRxiv (prépublication)

ASTIH: A collection of axon and myelin segmentation datasets from multiple histology studies

Armand Collin

Mathieu Boudreau

Large-scale analysis of axon and myelin morphometry in nervous tissues is fundamental to neuroscience research, yet manual quantification re… (voir plus)mains a profound bottleneck, limiting the scale and efficiency of studies. To address this, we introduce the Axon Segmentation Training Initiative for Histology (ASTIH), a publicly accessible resource designed to propel the development and validation of automated histomorphometry tools. ASTIH comprises five meticulously curated datasets, standardized for machine learning applications, featuring over 69,000 manually segmented axon fibers. These datasets exhibit significant diversity, spanning three microscopy modalities (TEM, SEM, bright-field), three species (mouse, rat, rabbit), and three distinct anatomical regions (brain, spinal cord, peripheral nerves) with varying pixel resolutions (from ~0.2 to 0.002

2026-02-13

Medical Imaging with Deep Learning (poster)

openreview.net

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

Enamundram Naga Karthik

Christoph S. Aigner

Élise Bannier

Josef Bednařík

Virginie Callot

Anna Combes

Armin Curt

Gergely David

Falk Eippert

Lynn Farner

Michael G Fehlings

Patrick Freund

Tobias Granberg

Cristina Granziera

Rhscir Network Imaging Group

Ulrike Horn

Tomáš Horák

Suzanne Humphreys … (voir 36 de plus)

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

Pierre-François Pradat

Alexandre Prat

Emanuele Pravatà

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 Andrada Treaba

Charidimos Tsagkas

Zachary Vavasour

Dimitri Van De Ville

Kenneth Arnold Weber II

Sarath Chandar

Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and … (voir plus)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 (publié)