Julien Cohen-Adad

Paul Hoareau

Maîtrise recherche - Polytechnique

paul.hoareau@mila.quebec

youssef.laatar@mila.quebec

Youssef Laatar

Maîtrise recherche - Polytechnique

nathan.molinier@mila.quebec

Nathan Molinier

Doctorat - Polytechnique

Publications

Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy

Abdul Al-Shawwa

Kalum Ost

David C. Anderson

Newton Cho

Nathan Evaniew

W. Bradley Jacobs

Allan R. Martin

Ranjeet Gaekwad

Saswati Tripathy

Jacques Bouchard

Steve Casha

Roger Cho

S. Duplessis

Peter Lewkonia

Fred Nicholls

Paul Salo

Alex Soroceanu

Ganesh Swamy

Kenneth Thomas

Michael Yang … (voir 2 de plus)

Julien Cohen‐Adad

David W. Cadotte

2024-03-31

The Spine Journal (publié)

Normalizing Spinal Cord Compression Morphometric Measures: Application in Degenerative Cervical Myelopathy

Sandrine Bédard

Maryam Seif

Armin Curt

Simon Schadings

M.Sc

Nikolai Pfender

Patrick Freund

Markus Hupp

The study introduced an automatic method for computation of normalized MSCC measures of cord compression from MRI scans, which is an importa… (voir plus)nt step towards better informed therapeutic decisions in DCM patients. The method is open-source and available in the Spinal Cord Toolbox v6.0.

2024-03-14

medRxiv (prépublication)

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

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

Julien Cohen-Adad … (voir 94 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

Elizabeth DuPré

Eric Earl

Oscar Estéban

Franklin W. Feingold

Guillaume Flandin

anthony galassi

Giuseppe Gallitto

Melanie Ganz

Rémi Gau

James Gholam

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

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

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 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

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neu… (voir plus)roscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

2024-02-19

Imaging Neuroscience (publié)

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

Sandrine Bédard

Miloš Keřkovský

Tomáš Rohan

Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of spinal co… (voir plus)rd 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.

2024-01-09

Imaging Neuroscience (publié)

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

Naga Karthik Enamundram

Andrew C. Smith

Dario Pfyffer

Simon Schading-Sassenhausen

Lynn Farner

Kenneth A. Weber II

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.

2024-01-02

MedRxiv (prépublication)

Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox

The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing cr… (voir plus)itical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).

2023-12-31

Magnetic Resonance in Medical Sciences (publié)

Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord

Haykel Snoussi

Julien Cohen‐Adad

Olivier Commowick

Benoit Combes

Élise Bannier

Slimane Tounekti

Anne Kerbrat

Christian Barillot

Emmanuel Caruyer

Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is… (voir plus) widely used for MS diagnosis and clinical follow‐up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball‐and‐Stick models.

2023-09-28

Brain and Behavior (publié)

Pontomedullary junction as a reference for spinal cord cross-sectional area: validation across neck positions

Sandrine Bédard

Maxime Bouthillier

Spinal cord cross-sectional area (CSA) is an important MRI biomarker to assess spinal cord atrophy in various neurodegenerative and traumati… (voir plus)c spinal cord diseases. However, the conventional method of computing CSA based on vertebral levels is inherently flawed, as the prediction of spinal levels from vertebral levels lacks reliability, leading to considerable variability in CSA measurements. Computing CSA from an intrinsic neuroanatomical reference, the pontomedullary junction (PMJ), has been proposed in previous work to overcome limitations associated with using a vertebral reference. However, the validation of this alternative approach, along with its variability across and within participants under variable neck extensions, remains unexplored. The goal of this study was to determine if the variability of CSA across neck flexions/extensions is reduced when using the PMJ, compared to vertebral levels. Ten participants underwent a 3T MRI T2w isotropic scan at 0.6 mm3 for 3 neck positions: extension, neutral and flexion. Spinal cord segmentation, vertebral labeling, PMJ labeling, and CSA were computed automatically while spinal segments were labeled manually. Mean coefficient of variation for CSA across neck positions was 3.99 ± 2.96% for the PMJ method vs. 4.02 ± 3.01% for manual spinal segment method vs. 4.46 ± 3.10% for the disc method. These differences were not statistically significant. The PMJ method was slightly more reliable than the disc-based method to compute CSA at specific spinal segments, although the difference was not statistically significant. This suggests that the PMJ can serve as a valuable alternative and reliable method for estimating CSA when a disc-based approach is challenging or not feasible, such as in cases involving fused discs in individuals with spinal cord injuries.

2023-08-18

Scientific Reports (publié)

An 8-channel Tx dipole and 20-channel Rx loop coil array for MRI of the cervical spinal cord at 7 Tesla

Nibardo Lopez-Rios

Kyle M. Gilbert

Daniel Papp

Gaspard Cereza

Alexandru Foias

Deshpande Rangaprakash

Markus W. May

Bastien Guerin

Lawrence L. Wald

Boris Keil

Jason P. Stockmann

Robert L. Barry

The quality of cervical spinal cord images can be improved by the use of tailored radiofrequency coil solutions for ultra-high field imaging… (voir plus); however, very few commercial and research 7 Tesla radiofrequency coils currently exist for the spinal cord, and in particular those with parallel transmit capabilities. This work presents the design, testing and validation of a pTx/Rx coil for the human neck and cervical/upper-thoracic spinal cord. The pTx portion is composed of 8 dipoles to ensure high homogeneity over this large region of the spinal cord. The Rx portion is made of 20 semi-adaptable overlapping loops to produce high Signal-to-noise ratio (SNR) across the patient population. The coil housing is designed to facilitate patient positioning and comfort, while being tight fitting to ensure high sensitivity. We demonstrate RF shimming capabilities to optimize B1+ uniformity, power efficiency and/or specific absorption rate (SAR) efficiency. B1+ homogeneity, SNR and g-factor was evaluated in adult volunteers and demonstrated excellent performance from the occipital lobe down to the T4-T5 level. We compared the proposed coil with two state-of-the-art head and head/neck coils, confirming its superiority in the cervical and upper-thoracic regions of the spinal cord. This coil solution therefore provides a convincing platform for producing the high image quality necessary for clinical and research scanning of the upper spinal cord.

2023-07-12

NMR in Biomedicine (publié)

Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

Reza Azad

Yiwei Jia

Ehsan Khodapanah Aghdam

Dorit Merhof

While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suff… (voir plus)er from limitations in capturing long-range dependencies. Transformer-based approaches are currently prevailing since they enlarge the reception field to model global contextual correlation. To further extract rich representations, some extensions of the U-Net employ multi-scale feature extraction and fusion modules and obtain improved performance. Inspired by this idea, we propose TransCeption for medical image segmentation, a pure transformer-based U-shape network featured by incorporating the inception-like module into the encoder and adopting a contextual bridge for better feature fusion. The design proposed in this work is based on three core principles: (1) The patch merging module in the encoder is redesigned with ResInception Patch Merging (RIPM). Multi-branch transformer (MB transformer) adopts the same number of branches as the outputs of RIPM. Combining the two modules enables the model to capture a multi-scale representation within a single stage. (2) We construct an Intra-stage Feature Fusion (IFF) module following the MB transformer to enhance the aggregation of feature maps from all the branches and particularly focus on the interaction between the different channels of all the scales. (3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective. Extensive experiments on multi-organ and skin lesion segmentation tasks present the superior performance of TransCeption compared to previous work. The code is publicly available at https://github.com/mindflow-institue/TransCeption.

2023-01-24

ArXiv (prépublication)

Label fusion and training methods for reliable representation of inter-rater uncertainty

Andréanne Lemay

Charley Gros

Enamundram Naga Karthik

Medical tasks are prone to inter-rater variability due to multiple factors such as image quality, professional experience and training, or g… (voir plus)uideline clarity. Training deep learning networks with annotations from multiple raters is a common practice that mitigates the model's bias towards a single expert. Reliable models generating calibrated outputs and reflecting the inter-rater disagreement are key to the integration of artificial intelligence in clinical practice. Various methods exist to take into account different expert labels. We focus on comparing three label fusion methods: STAPLE, average of the rater's segmentation, and random sampling of each rater's segmentation during training. Each label fusion method is studied using both the conventional training framework and the recently published SoftSeg framework that limits information loss by treating the segmentation task as a regression. Our results, across 10 data splittings on two public datasets, indicate that SoftSeg models, regardless of the ground truth fusion method, had better calibration and preservation of the inter-rater rater variability compared with their conventional counterparts without impacting the segmentation performance. Conventional models, i.e., trained with a Dice loss, with binary inputs, and sigmoid/softmax final activate, were overconfident and underestimated the uncertainty associated with inter-rater variability. Conversely, fusing labels by averaging with the SoftSeg framework led to underconfident outputs and overestimation of the rater disagreement. In terms of segmentation performance, the best label fusion method was different for the two datasets studied, indicating this parameter might be task-dependent. However, SoftSeg had segmentation performance systematically superior or equal to the conventionally trained models and had the best calibration and preservation of the inter-rater variability.

2023-01-17

Machine Learning for Biomedical Imaging (publié)

HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation

Moein Heidari

Amirhossein Kazerouni

Milad Soltany

Reza Azad

Ehsan Khodapanah Aghdam

Dorit Merhof

Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation … (voir plus)in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation. Specifically, we design two multi-scale feature representations using the seminal Swin Transformer module and a CNN-based encoder. To secure a fine fusion of global and local features obtained from the two aforementioned representations, we propose a Double-Level Fusion (DLF) module in the skip connection of the encoder-decoder structure. Extensive experiments on various medical image segmentation datasets demonstrate the effectiveness of HiFormer over other CNN-based, transformer-based, and hybrid methods in terms of computational complexity, and quantitative and qualitative results. Our code is publicly available at: https://github.com/amirhossein-kz/HiFormer

2023-01-01

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)