Portrait of Julien Cohen-Adad

Julien Cohen-Adad

Associate Academic Member
Associate Professor, Polytechnique Montréal, Electrical Engineering Department
Adjunct Professor, Université de Montréal, Department of Neuroscience
Research Topics
Medical Machine Learning
Website
Google Scholar
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Biography

Julien Cohen-Adad is a professor at Polytechnique Montréal and the associate director of the Neuroimaging Functional Unit at Université de Montréal. He is also the Canada Research Chair in Quantitative Magnetic Resonance Imaging.

His research focuses on advancing neuroimaging methods with the help of AI. Some examples of projects are:

- Multi-modal training for medical imaging tasks (segmentation of pathologies, diagnosis, etc.)

- Adding prior from MRI physics to improve model generalization

- Incorporating uncertainty measures to deal with inter-rater variability

- Continuous learning strategies when data sharing is restricted

- Bringing AI methods into clinical radiology routine via user-friendly software solutions

Cohen-Adad also leads multiple open-source software projects that are benefiting the research and clinical community (see neuro.polymtl.ca/software.html). In short, he loves MRI with strong magnets, neuroimaging, programming and open science!

Current Students

Rohan Banerjee
Collaborating Alumni - Polytechnique Montréal
Co-supervisor :
Gauthier Gidel
Website
Github
Google Scholar
Kahina Baouche
Research Intern - Polytechnique Montréal
Github
Pierre Louis Benveniste
PhD - Polytechnique Montréal
Co-supervisor :
Hervé Lombaert
Website
Github
Google Scholar
Armand Collin
PhD - Polytechnique Montréal
Github
Thomas Dagonneau
Master's Research - Polytechnique Montréal
Naga Karthik Enamundram
PhD - Polytechnique Montréal
Co-supervisor :
Sarath Chandar
Website
Github
Google Scholar
Paul Hoareau
Master's Research - Polytechnique Montréal
Github
Nilser Laines Medina
PhD - Polytechnique Montréal
Nathan Molinier
PhD - Polytechnique Montréal
Github
Kalum Ost
Collaborating researcher
Arthur Toulouse
Master's Research - Polytechnique Montréal
Github
Jan Valosek
Postdoctorate - Polytechnique Montréal
Website
Github
Google Scholar

Publications

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 … (see 334 more)
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
Julien Cohen-Adad
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
W. Pan
Winnie Pang
Bogyu Park
Vivek G. Pawar
K. 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
W. 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 Ashraf Zeineldin
Dewen Zeng
Jianpeng Zhang
Bokai Zhang
Jiapeng Zhang
Fangxi Zhang
Huahong Zhang
Zhongchen Zhao
Zixuan Zhao
Jia Zhao
Can Zhao
Q. Zheng
Yuheng Zhi
Ziqi Zhou
Baosheng Zou
Klaus Maier-Hein
PAUL F. JÄGER
Annette Kopp-Schneider
Lena Maier-Hein
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practic… (see more)e. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.
2022-12-16
ArXiv (preprint)
doi.org
arxiv.org
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 … (see 334 more)
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
Julien Cohen-Adad
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
W. Pan
Winnie Pang
Bogyu Park
Vivek G. Pawar
K. 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
W. 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 Ashraf Zeineldin
Dewen Zeng
Jianpeng Zhang
Bokai Zhang
Jiapeng Zhang
Fangxi Zhang
Huahong Zhang
Zhongchen Zhao
Zixuan Zhao
Jia Zhao
Can Zhao
Q. Zheng
Yuheng Zhi
Ziqi Zhou
Baosheng Zou
Klaus Maier-Hein
PAUL F. JÄGER
Annette Kopp-Schneider
Lena Maier-Hein
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practic… (see more)e. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.
2022-12-16
ArXiv (preprint)
doi.org
arxiv.org
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 … (see 334 more)
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
Julien Cohen-Adad
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
2022-12-16
ArXiv (preprint)
doi.org
arxiv.org
Histology-informed automatic parcellation of white matter tracts in the rat spinal cord
Harris Nami
Christian S. Perone
Julien Cohen-Adad
The white matter is organized into “tracts” or “bundles,” which connect different parts of the central nervous system. Knowing where… (see more) 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.
2022-11-29
Frontiers in Neuroanatomy (published)
doi.org
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
2022-11-28
Magnetic Resonance in Medicine (published)
doi.org
Shimming toolbox: An open‐source software toolbox for B0 and B1 shimming in MRI
Alexandre D'Astous
Gaspard Cereza
Daniel Papp
Kyle M. Gilbert
Jason P. Stockmann
Eva Alonso‐Ortiz
Julien Cohen-Adad
2022-11-28
Magnetic Resonance in Medicine (published)
doi.org
Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction
Sandrine Bédard
Julien Cohen-Adad
2022-11-02
Frontiers in Neuroimaging (published)
doi.org
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
Sarath Chandar
Julien Cohen-Adad
Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent y… (see more)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
2022-10-27
ArXiv (preprint)
doi.org
arxiv.org
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 -- Ex vivo imaging
Kurt G Schilling
Francesco Grussu
Andrada Ianus
Brian Hansen
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Ben Jeurissen
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn … (see 30 more)
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Ileana O. Jelescu
2022-09-27
ArXiv (preprint)
doi.org
arxiv.org
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging
Ileana O. Jelescu
Francesco Grussu
Andrada Ianus
Brian Hansen
Manisha Aggarwal
Stijn Michielse
Fatima Nasrallah
Warda Syeda
Nian Wang
Jelle Veraart
Alard Roebroeck
Andrew F. Bagdasarian
Cornelius Eichner
Farshid Sepehrband
Jan Zimmermann
Ben Jeurissen
Lucio Frydman
Yohan van de Looij
David Hike
Jeff F. Dunn … (see 30 more)
Karla Miller
Bennett Landman
Noam Shemesh
Arthur Anderson
Emilie McKinnon
Shawna Farquharson
Flavio Dell’Acqua
Carlo Pierpaoli
Ivana Drobnjak
Alexander Leemans
Kevin D. Harkins
Maxime Descoteaux
Duan Xu
Mathieu D. Santin
Samuel C. Grant
Andre Obenaus
Gene S. Kim
Dan Wu
Denis Le Bihan
Stephen J. Blackband
Luisa Ciobanu
Els Fieremans
Ruiliang Bai
Trygve B. Leergaard
Jiangyang Zhang
Tim B. Dyrby
G. Allan Johnson
Julien Cohen-Adad
Matthew D. Budde
Kurt G Schilling
The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and val… (see more)idation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
2022-09-26
ArXiv (preprint)
doi.org
arxiv.org
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Reza Azad
Moein Heidari
Julien Cohen-Adad
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… (see more)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
2022-09-16
Predictive Intelligence in Medicine (published)
doi.org
arxiv.org
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
Nathan
B. 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… (see more) 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.
2022-07-07
medRxiv (preprint)
doi.org