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

ivadomed: A Medical Imaging Deep Learning Toolbox

Charley Gros

Andréanne Lemay

Olivier Vincent

Lucas Rouhier

Anthime Bucquet

ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging… (voir plus) data. The package includes APIs, command-line tools, documentation, and tutorials. ivadomed also includes pre-trained models such as spinal tumor segmentation and vertebral labeling. Original features of ivadomed include a data loader that can parse image metadata (e.g., acquisition parameters, image contrast, resolution) and subject metadata (e.g., pathology, age, sex) for custom data splitting or extra information during training and evaluation. Any dataset following the Brain Imaging Data Structure (BIDS) convention will be compatible with ivadomed without the need to manually organize the data, which is typically a tedious task. Beyond the traditional deep learning methods, ivadomed features cutting-edge architectures, such as FiLM and HeMis, as well as various uncertainty estimation methods (aleatoric and epistemic), and losses adapted to imbalanced classes and non-binary predictions. Each step is conveniently configurable via a single file. At the same time, the code is highly modular to allow addition/modification of an architecture or pre/post-processing steps. Example applications of ivadomed include MRI object detection, segmentation, and labeling of anatomical and pathological structures. Overall, ivadomed enables easy and quick exploration of the latest advances in deep learning for medical imaging applications. ivadomed's main project page is available at https://ivadomed.org.

2022-12-31

Zenodo (publié)

Biomedical image analysis competitions: The state of current participation practice

Matthias Eisenmann

Annika Reinke

Vivienn Weru

Minu Dietlinde Tizabi

Fabian Isensee

T. Adler

PATRICK GODAU

Veronika Cheplygina

Michal Kozubek

Sharib Ali

Anubha Gupta

Jan. Kybic

Alison Professor Noble

Carlos Ortiz de Sol'orzano

Samiksha Pachade

Caroline Petitjean

Daniel Sage

Donglai Wei

Elizabeth Wilden

Deepak Alapatt … (voir 334 de plus)

Vincent Andrearczyk

Ujjwal Baid

Spyridon Bakas

Niranjan Balu

Sophia Bano

Vivek Singh Bawa

Jorge Bernal

Sebastian Bodenstedt

Alessandro Casella

Jinwook Choi

Olivier Commowick

M. Daum

Adrien Depeursinge

Reuben Dorent

J. Egger

H. Eichhorn

Sandy Engelhardt

Melanie Ganz

Gabriel Girard

Lasse Donovan Hansen

Mattias Paul Heinrich

Nicholas Heller

Alessa Hering

Arnaud Huaulm'e

Hyunjeong Kim

Bennett Landman

Hongwei Bran Li

Jianning Li

Junfang Ma

Anne L. Martel

Carlos Mart'in-Isla

Bjoern Menze

Chinedu Innocent Nwoye

Valentin Oreiller

Nicolas Padoy

Sarthak Pati

Kelly Payette

Carole H. Sudre

K. V. Wijnen

Armine Vardazaryan

Tom Kamiel Magda Vercauteren

Martin Wagner

Chuanbo Wang

Moi Hoon Yap

Zeyun Yu

Chuner Yuan

Maximilian Zenk

Aneeq Zia

David Zimmerer

Rina Bao

Chanyeol Choi

Andrew Cohen

Oleh Dzyubachyk

Adrian Galdran

Tianyuan Gan

Tianqi Guo

Pradyumna Gupta

M. Haithami

Edward Ho

Ikbeom Jang

Zhili Li

Zheng Luo

Filip Lux

Sokratis Makrogiannis

Dominikus Muller

Young-Tack Oh

Subeen Pang

Constantin Pape

Gorkem Polat

Charlotte Rosalie Reed

Kanghyun Ryu

Tim Scherr

Vajira L. Thambawita

Haoyu Wang

Xinliang Wang

Kele Xu

H.-I. Yeh

Doyeob Yeo

Yi Yuan

Yan Zeng

Xingwen Zhao

Julian Ronald Abbing

Jannes Adam

Nagesh Adluru

Niklas Agethen

S. Ahmed

Yasmina Al Khalil

Mireia Alenya

Esa J. Alhoniemi

C. An

Talha E Anwar

Tewodros Arega

Netanell Avisdris

D. Aydogan

Yi-Shi Bai

Maria Baldeon Calisto

Berke Doga Basaran

Marcel Beetz

Cheng Bian

Hao-xuan Bian

Kevin Blansit

Louise Bloch

Robert Bohnsack

Sara Bosticardo

J. Breen

Mikael Brudfors

Raphael Brungel

Mariano Cabezas

Alberto Cacciola

Zhiwei Chen

Yucong Chen

Dan Chen

Minjeong Cho

Min-Kook Choi

Chuantao Xie Chuantao Xie

Dana Cobzas

Jorge Corral Acero

Sujit Kumar Das

Marcela de Oliveira

Hanqiu Deng

Guiming Dong

Lars Doorenbos

Cory Efird

Di Fan

Mehdi Fatan Serj

Alexandre Fenneteau

Lucas Fidon

Patryk Filipiak

Ren'e Finzel

Nuno Renato Freitas

C. Friedrich

Mitchell J. Fulton

Finn Gaida

Francesco Galati

Christoforos Galazis

Changna Gan

Zheyao Gao

Sheng Gao

Matej Gazda

Beerend G. A. Gerats

Neil Getty

Adam Gibicar

Ryan J. Gifford

Sajan Gohil

Maria Grammatikopoulou

Daniel Grzech

Orhun Guley

Timo Gunnemann

Chun-Hai Guo

Sylvain Guy

Heonjin Ha

Luyi Han

Ilseok Han

Ali Hatamizadeh

Tianhai He

Ji-Wu Heo

Sebastian Hitziger

SeulGi Hong

Seungbum Hong

Rian Huang

Zi-You Huang

Markus Huellebrand

Stephan Huschauer

M. Hussain

Tomoo Inubushi

Ece Isik Polat

Mojtaba Jafaritadi

Seonghun Jeong

Bailiang Jian

Yu Jiang

Zhifan Jiang

Yu Jin

Smriti Joshi

A. Kadkhodamohammadi

R. A. Kamraoui

Inhak Kang

Jun-Su Kang

Davood Karimi

April Ellahe Khademi

Muhammad Irfan Khan

Suleiman A. Khan

Rishab Khantwal

Kwang-Ju Kim

Timothy Lee Kline

Satoshi Kondo

Elina Kontio

Adrian Krenzer

Artem Kroviakov

Hugo J. Kuijf

Satyadwyoom Kumar

Francesco La Rosa

Abhishek Lad

Doohee Lee

Minho Lee

Chiara Lena

Hao Li

Ling Li

Xingyu Li

F. Liao

Kuan-Ya Liao

Arlindo L. Oliveira

Chaonan Lin

Shanhai Lin

Akis Linardos

M. Linguraru

Han Liu

Tao Liu

Dian Liu

Yanling Liu

Joao Lourencco-Silva

Jing Lu

Jia Lu

Imanol Luengo

Christina Bach Lund

Huan Minh Luu

Yingqi Lv

Uzay Macar

Leon Maechler

L. SinaMansour

Kenji Marshall

Moona Mazher

Richard McKinley

Alfonso Medela

Felix Meissen

Mingyuan Meng

Dylan Bradley Miller

S. Mirjahanmardi

Arnab Kumar Mishra

Samir Mitha

Hassan Mohy-ud-Din

Tony C. W. Mok

Gowtham Krishnan Murugesan

Enamundram Naga Karthik

Sahil Nalawade

Jakub Nalepa

M. Naser

Ramin Nateghi

Hammad Naveed

Quang-Minh Nguyen

Cuong Nguyen Quoc

Brennan Nichyporuk

Bruno Oliveira

David Owen

Jimut Bahan Pal

Junwen Pan

Wei-Dong Pan

Winnie Pang

Bogyu Park

Vivek G. Pawar

Kamlesh Pawar

Michael Peven

Lena Philipp

Tomasz Pieciak

Szymon S Płotka

Marcel Plutat

Fattane Pourakpour

Domen Prelovznik

K. Punithakumar

Abdul Qayyum

Sandro Queir'os

Arman Rahmim

Salar Razavi

Jintao Ren

Mina Rezaei

Jonathan Adam Rico

ZunHyan Rieu

Markus Rink

Johannes Roth

Yusely Ruiz-gonzalez

Numan Saeed

Anindo Saha

Mostafa M. Sami Salem

Ricardo Sanchez-matilla

Kurt G Schilling

Weizhen Shao

Zhiqiang Shen

Ruize Shi

Pengcheng Shi

Daniel Sobotka

Th'eodore Soulier

Bella Specktor Fadida

D. Stoyanov

Timothy Sum Hon Mun

Xiao-Fu Sun

Rong Tao

Franz Thaler

Antoine Th'eberge

Felix Thielke

Helena R. Torres

K. Wahid

Jiacheng Wang

Yifei Wang

Wei David Wang

Xiong Jun Wang

Jianhui Wen

Ning Wen

Marek Wodziński

Yehong Wu

Fangfang Xia

Tianqi Xiang

Cheng Xiaofei

Lizhang Xu

Tingting Xue

Yu‐Xia Yang

Lingxian Yang

Kai Yao

Huifeng Yao

Amirsaeed Yazdani

Michael Yip

Hwa-Seong Yoo

Fereshteh Yousefirizi

Shu-Fen Yu

Lei Yu

Jonathan Zamora

Ramy A. Zeineldin

Dewen Zeng

Jianpeng Zhang

Bokai Zhang

Jiapeng Zhang

Fangxi Zhang

Huahong Zhang

Zhongchen Zhao

Zixuan Zhao

Jia Zhao

Can Zhao

Qiuyue Zheng

Yuheng Zhi

Ziqi Zhou

Baosheng Zou

Klaus Maier-Hein

PAUL F. JÄGER

Annette Kopp-Schneider

Lena Maier-Hein

2022-12-15

ArXiv (prépublication)

Histology-informed automatic parcellation of white matter tracts in the rat spinal cord

Harris Nami

Christian S. Perone

The white matter is organized into “tracts” or “bundles,” which connect different parts of the central nervous system. Knowing where… (voir plus) these tracts are located in each individual is important for understanding the cause of potential sensorial, motor or cognitive deficits and for developing appropriate treatments. Traditionally, tracts are found using tracer injection, which is a difficult, slow and poorly scalable technique. However, axon populations from a given tract exhibit specific characteristics in terms of morphometrics and myelination. Hence, the delineation of tracts could, in principle, be done based on their morphometry. The objective of this study was to generate automatic parcellation of the rat spinal white matter tracts using the manifold information from scanning electron microscopy images of the entire spinal cord. The axon morphometrics (axon density, axon diameter, myelin thickness and g-ratio) were computed pixelwise following automatic axon segmentation using AxonSeg. The parcellation was based on an agglomerative clustering algorithm to group the tracts. Results show that axon morphometrics provide sufficient information to automatically identify some white matter tracts in the spinal cord, however, not all tracts were correctly identified. Future developments of microstructure quantitative MRI even bring hope for a personalized clustering of white matter tracts in each individual patient. The generated atlas and the associated code can be found at https://github.com/neuropoly/tract-clustering.

2022-11-28

Frontiers in Neuroanatomy (publié)

Shimming toolbox: An open‐source software toolbox for <scp>B0</scp> and <scp>B1</scp> shimming in MRI

Alexandre D'Astous

Gaspard Cereza

Daniel Papp

Kyle M. Gilbert

Jason P. Stockmann

Eva Alonso‐Ortiz

Julien Cohen‐Adad

Introduce Shimming Toolbox ( https://shimming‐toolbox.org), an open‐source software package for prototyping new methods and performing s… (voir plus)tatic, dynamic, and real‐time B0 shimming as well as B1 shimming experiments. Shimming Toolbox features various field mapping techniques, manual and automatic masking for the brain and spinal cord, B0 and B1 shimming capabilities accessible through a user‐friendly graphical user interface. Validation of Shimming Toolbox was demonstrated in three scenarios: (i) B0 dynamic shimming in the brain at 7T using custom AC/DC coils, (ii) B0 real‐time shimming in the spinal cord at 3T, and (iii) B1 static shimming in the spinal cord at 7T. Shimming Toolbox provides an open‐source platform where researchers can collaborate, prototype and conveniently test B0 and B1 shimming experiments. Future versions will include additional field map preprocessing techniques, optimization algorithms, and compatibility across multiple MRI manufacturers.

2022-11-27

Magnetic Resonance in Medicine (inconnu)

Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction

Sandrine Bédard

Julien Cohen‐Adad

Spinal cord cross-sectional area (CSA) is a relevant biomarker to assess spinal cord atrophy in neurodegenerative diseases. However, the con… (voir plus)siderable inter-subject variability among healthy participants currently limits its usage. Previous studies explored factors contributing to the variability, yet the normalization models required manual intervention and used vertebral levels as a reference, which is an imprecise prediction of the spinal levels. In this study we implemented a method to measure CSA automatically from a spatial reference based on the central nervous system (the pontomedullary junction, PMJ), we investigated factors to explain variability, and developed normalization strategies on a large cohort (N = 804). Following automatic spinal cord segmentation, vertebral labeling and PMJ labeling, the spinal cord CSA was computed on T1w MRI scans from the UK Biobank database. The CSA was computed using two methods. For the first method, the CSA was computed at the level of the C2–C3 intervertebral disc. For the second method, the CSA was computed at 64 mm caudally from the PMJ, this distance corresponding to the average distance between the PMJ and the C2–C3 disc across all participants. The effect of various demographic and anatomical factors was explored, and a stepwise regression found significant predictors; the coefficients of the best fit model were used to normalize CSA. CSA measured at C2–C3 disc and using the PMJ differed significantly (paired t-test, p-value = 0.0002). The best normalization model included thalamus, brain volume, sex and the interaction between brain volume and sex. The coefficient of variation went down for PMJ CSA from 10.09 (without normalization) to 8.59%, a reduction of 14.85%. For CSA at C2–C3, it went down from 9.96 to 8.42%, a reduction of 15.13 %. This study introduces an end-to-end automatic pipeline to measure and normalize cord CSA from a neurological reference. This approach requires further validation to assess atrophy in longitudinal studies. The inter-subject variability of CSA can be partly accounted for by demographics and anatomical factors.

2022-11-01

Frontiers in Neuroimaging (publié)

Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?

Enamundram Naga Karthik

Anne Kerbrat

Pierre Labauge

Tobias Granberg

Jason F. Talbott

Daniel S Reich

Massimo Filippi

Rohit Bakshi

Virginie Callot

A. Chandar

Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent y… (voir plus)ears. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset, which does not generalize well. Instead, the model should learn across datasets arriving sequentially from different hospitals by building upon the characteristics of lesions in a continual manner. In this regard, we explore experience replay, a well-known continual learning method, in the context of MS lesion segmentation across multi-contrast data from 8 different hospitals. Our experiments show that replay is able to achieve positive backward transfer and reduce catastrophic forgetting compared to sequential fine-tuning. Furthermore, replay outperforms the multi-domain training, thereby emerging as a promising solution for the segmentation of MS lesions. The code is available at this link: https://github.com/naga-karthik/continual-learning-ms

2022-10-26

arXiv (prépublication)

Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach

Reza Azad

Moein Heidari

Ehsan Adeli

Dorit Merhof

Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related disea… (voir plus)ses such as osteoporosis, vertebral fractures, and intervertebral disc herniation. To date, various approaches have been developed in the literature which routinely relies on detecting the discs as the primary step. A disadvantage of many cohort studies is that the localization algorithm also yields false-positive detections. In this study, we aim to alleviate this problem by proposing a novel U-Net-based structure to predict a set of candidates for intervertebral disc locations. In our design, we integrate the image shape information (image gradients) to encourage the model to learn rich and generic geometrical information. This additional signal guides the model to selectively emphasize the contextual representation and suppress the less discriminative features. On the post-processing side, to further decrease the false positive rate, we propose a permutation invariant 'look once' model, which accelerates the candidate recovery procedure. In comparison with previous studies, our proposed approach does not need to perform the selection in an iterative fashion. The proposed method was evaluated on the spine generic public multi-center dataset and demonstrated superior performance compared to previous work. We have provided the implementation code in https://github.com/rezazad68/intervertebral-lookonce

2022-09-15

Predictive Intelligence in Medicine (publié)

Advanced MRI scan acquisition metrics improve baseline disease severity predictions compared to traditional community MRI scan metrics

Abdul Al-Shawwa

Kalum Ost

David W. Cadotte

David Anderson

Nathan Evaniew

Bradley Jacobs

Julien Cohen‐Adad

Degenerative Cervical Myelopathy (DCM) is the functional derangement of the spinal cord and acts as one of the most common atraumatic spinal… (voir plus) cord injuries. Magnetic resonance imaging (MRI) are key in confirming the diagnosis of DCM in patients, though the utilization of higher fidelity magnetic resonance imaging scans and their integration into machine learning models remains largely unexplored. This study looks at the predictive ability of common community MRI scans in comparison to high fidelity scans in disease diagnosis. We hypothesize that the utilization of higher fidelity "advanced" MRI scans will increase the effectiveness of machine learning models predicting DCM severity. Through the utilization of Random Forest Classifiers, we have been able to predict disease severity with 41.8% accuracy in current community MRI scans and 63.9% in the advanced MRI scans. Furthermore, across the different predictive model variations tested, the advanced MRI scans consistently produced higher prediction accuracies compared to the community MRI counterparts. These results support our hypothesis and indicate that machine learning models have the potential to predict disease severity. However, neither performed well enough to be considered for use in clinical practice, indicating that the utilization of more sophisticated machine models may be required for these purposes.

2022-07-06

medRxiv (prépublication)

Relationship Between Arterial Stiffness Index, Pulse Pressure, and Magnetic Resonance Imaging Markers of White Matter Integrity: A UK Biobank Study

Atef Badji

Hélène Girouard

Alzheimer’s disease and dementia in general constitute one of the major public health problems of the 21st century. Research in arterial s… (voir plus)tiffness and pulse pressure (PP) play an important role in the quest to reduce the risk of developing dementia through controlling modifiable risk factors. The aim of the study is to investigate the association between peripheral PP, arterial stiffness index (ASI) and brain integrity, and to discover if ASI is a better predictor of white matter integrity than peripheral PP. 17,984 participants 63.09 ± 7.31 from the UK Biobank were used for this study. ASI was estimated using infrared light (photoplethysmography) and peripheral PP was calculated by subtracting the diastolic from the systolic brachial blood pressure value. Measure of fractional anisotropy (FA) was obtained from diffusion imaging to estimate white matter microstructural integrity. White matter hyperintensities were segmented from the combined T1 and T2-weighted FLAIR images as a measure of irreversible white matter damage. An important finding is that peripheral PP better predicts white matter integrity when compared to ASI. This finding is consistent until 75 years old. Interestingly, no significant relationship is found between either peripheral PP or ASI and white matter integrity after 75 years old. These results suggest that ASI from plethysmography should not be used to estimate cerebrovascular integrity in older adults and further question the relationship between arterial stiffness, blood pressure, and white matter damage after the age of 75 years old.

2022-06-20

Frontiers in Aging Neuroscience (publié)

Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI

Agah Karakuzu

Labonny Biswas

Nikola Stikov

We conclude that vendor-neutral workflows are feasible and compatible with clinical MRI scanners. The significant reduction of inter-vendor … (voir plus)variability using vendor-neutral sequences has important implications for qMRI research and for the reliability of multicenter clinical trials.

2022-06-02

Magnetic Resonance in Medicine (inconnu)

Diffusion Kurtosis Imaging of neonatal Spinal Cord in clinical routine

Rosella Trò

Monica Roascio

Domenico Tortora

Mariasavina Severino

Andrea Rossi

Julien Cohen‐Adad

Marco Fato

Gabriele Arnulfo

Diffusion Kurtosis Imaging (DKI) has undisputed advantages over more classical diffusion Magnetic Resonance Imaging (dMRI), as witnessed by … (voir plus)a fast-increasing number of clinical applications and software packages widely adopted in brain imaging domain. Despite its power in probing tissue microstructure compared to conventional MRI, DKI is still largely underutilized in Spinal Cord (SC) imaging because of its inherently demanding technological requirements. If state-of-the-art hardware advancements have recently allowed to make great strides in applying this emerging method to adult and child SC, the same does not apply to neonatal setting. Indeed, amplified technical issues related to SC district in this age range have made this field still unexplored. However, results arising from recent application of DKI to adult and child SC are promising enough to suggest how informative this technique would be in investigating newborns, too. Due to its extreme sensitivity to non-gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, and not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between SC region and the above brain, managing to apply such a method to neonatal cohort becomes of utmost importance. In this work, we illustrate the first semi-automated pipeline for handling with DKI data of neonatal SC, from acquisition setting to estimation of diffusion (DTI & DKI) measures, through accurate adjustment of processing algorithms customized for adult SC. Each processing step of this pipeline, built on Spinal Cord Toolbox (SCT) software, has undergone Quality Control check by supervision of an expert pediatric neuroradiologist, and the overall procedure has preliminarily been tested in a pilot clinical case study. Results of this application agree with findings achieved in a corresponding adult survey, thus confirming validity of adopted pipeline and diagnostic value of DKI in pediatrics. This novel tool hence paves the wave for extending its application also to other promising advanced dMRI models, such as Neurite Orientation Dispersion and Density Imaging (NODDI), and to a wider range of potential clinical applications concerning neonatal period.

2022-05-22

Frontiers in Radiology (publié)

An interactive meta-analysis of MRI biomarkers of myelin

Matteo Mancini

Agah Karakuzu

Mara Cercignani

Thomas E Nichols

Nikola Stikov

Several MRI measures have been proposed as in vivo biomarkers of myelin, each with applications ranging from plasticity to pathology. Despit… (voir plus)e the availability of these myelin-sensitive modalities, specificity and sensitivity have been a matter of discussion. Debate about which MRI measure is the most suitable for quantifying myelin is still ongoing. In this study, we performed a systematic review of published quantitative validation studies to clarify how different these measures are when compared to the underlying histology. We analyzed the results from 43 studies applying meta-analysis tools, controlling for study sample size and using interactive visualization ( https://neurolibre.github.io/myelin-meta-analysis ). We report the overall estimates and the prediction intervals for the coefficient of determination and find that MT and relaxometry-based measures exhibit the highest correlations with myelin content. We also show which measures are, and which measures are not statistically different regarding their relationship with histology.

2022-04-19

NeuroLibre Reproducible Preprint Server (publié)