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
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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

Publications

Dynamic shimming in the cervical spinal cord for multi-echo gradient-echo imaging at 3 T
Eva Alonso‐Ortiz
Daniel Papp
A. D'Astous
Julien Cohen-Adad
2023-03-01
Neuroimage. Reports (published)
doi.org
arxiv.org
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
D. Rangaprakash
Markus W. May
Bastien Guerin
Lawrence L. Wald
Boris Keil
Jason P. Stockmann
Robert L. Barry
Julien Cohen-Adad
2023-02-09
bioRxiv (preprint)
doi.org
Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach
Reza Azad
Yiwei Jia
Ehsan Khodapanah Aghdam
Julien Cohen-Adad
Dorit Merhof
While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness, they suff… (see more)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.
2023-01-25
ArXiv (preprint)
doi.org
arxiv.org
Label fusion and training methods for reliable representation of inter-rater uncertainty
Andreanne Lemay
Charley Gros
Julien Cohen-Adad
Enamundram Naga Karthik
2023-01-18
Machine Learning for Biomedical Imaging (published)
doi.org
arxiv.org
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Moein Heidari
Amirhossein Kazerouni
Milad Soltany
Reza Azad
Ehsan Khodapanah Aghdam
Julien Cohen-Adad
Dorit Merhof
Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation … (see more)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, quantitative and qualitative results. Our code is publicly available at GitHub.
2023-01-02
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (published)
doi.org
arxiv.org
Contrast-agnostic deep learning–based registration pipeline: Validation in spinal cord multimodal MRI data
E. Beal
Julien Cohen-Adad
2023-01-01
Aperture Neuro (published)
doi.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
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
Relationship Between Arterial Stiffness Index, Pulse Pressure, and Magnetic Resonance Imaging Markers of White Matter Integrity: A UK Biobank Study
Atef Badji
Julien Cohen-Adad
Hélène Girouard
2022-06-21
Frontiers in Aging Neuroscience (published)
doi.org
NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline
Vicente Enguix
Jeanette Kenley
David Luck
Julien Cohen-Adad
Gregory Anton Lodygensky
Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its … (see more)integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.
2022-06-17
Frontiers in Neuroinformatics (published)
doi.org
arxiv.org
Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data
Marie-Hélène Bourget
Lee Kamentsky
Satrajit S. Ghosh
Giacomo Mazzamuto
Alberto Lazari
Christopher J. Markiewicz
Robert Oostenveld
Guiomar Niso
Yaroslav O. Halchenko
Ilona Lipp
Sylvain Takerkart
Paule-Joanne Toussaint
Ali R. Khan
Gustav Nilsonne
Filippo Maria Castelli
Stefan Ross Eric Franklin Anthony Rémi Christopher J. Taylor Appelhoff
Julien Cohen-Adad
The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusab… (see more)le way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.
2022-04-19
Frontiers in Neuroscience (published)
doi.org