Publications

Adaptation, Translation, and Validation of a Patient-Reported Experience Measure for Children and Young People for the Canadian Context.

Zanib Nafees

Julia Ferreira

Elena Guadagno

Jo Wray

Agneta Anderzén-Carlsson

Dan Poenaru

2024-02-01

Journal of Pediatric Surgery (published)

doi.org

Adaptation, Translation, and Validation of a Patient-Reported Experience Measure for Children and Young People for the Canadian Context

Zanib Nafees

Julia Ferreira

Elena Guadagno

Jo Wray

Agneta Anderzén-Carlsson

Dan Poenaru

2024-02-01

Journal of Pediatric Surgery (published)

doi.org

Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning

Mohammad Javad Darvishi Bayazi

Mohammad S. Ghaemi

Timothee LESORT

Md Rifat Arefin

Jocelyn Faubert

Irina Rish

2024-02-01

Computers in Biology and Medicine (published)

doi.org

arxiv.org

Author Correction: BCG immunization induces CX3CR1hi effector memory T cells to provide cross-protection via IFN-γ-mediated trained immunity.

Kim A. Tran

Erwan Pernet

Mina Sadeghi

Jeffrey Downey

Julia Chronopoulos

Elizabeth Lapshina

Oscar Tsai

Eva Kaufmann

Jun Ding

Maziar Divangahi

2024-02-01

Nature Immunology (published)

doi.org

Automatic segmentation of the spinal cord nerve rootlets

Jan Valosek

Theo Mathieu

Raphaëlle Schlienger

Olivia S. Kowalczyk

Julien Cohen-Adad

Abstract Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the … (see more)spinal cord. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access 3T MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from three datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 ± 0.16 (mean ± standard deviation across testing images and rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation ≤ 1.41%), as well as low inter-session variability (coefficient of variation ≤ 1.30%), indicating stable predictions across different MRI vendors, sites, and sessions. The proposed methodology is open-source and readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.

2024-02-01

ArXiv (preprint)

doi.org

arxiv.org

Automatic segmentation of the spinal cord nerve rootlets

Jan Valosek

Theo Mathieu

Raphaëlle Schlienger

Olivia S. Kowalczyk

Julien Cohen-Adad

Abstract Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the … (see more)spinal cord. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access 3T MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from three datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 ± 0.16 (mean ± standard deviation across testing images and rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation ≤ 1.41%), as well as low inter-session variability (coefficient of variation ≤ 1.30%), indicating stable predictions across different MRI vendors, sites, and sessions. The proposed methodology is open-source and readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.

2024-02-01

ArXiv (preprint)

doi.org

arxiv.org

Co-developing The Canadian MPS Registry: A longitudinal rare disease patient registry

John J. Mitchell

Michal Inbar-Feigenberg

Kim Angel

Pranesh Chakraborty

Monica Lamoureux

John Adams

Beth K. Potter