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Jan Valosek

jan.valosek@mila.quebec
Postdoctorate - Polytechnique Montréal
Supervisor
Julien Cohen-Adad
Website
Github
Google Scholar

Publications

Normalizing Spinal Cord Compression Morphometric Measures: Application in Degenerative Cervical Myelopathy
Sandrine Bédard
Jan Valošek
Maryam Seif PhD
Armin Curt PhD
Simon Schading Md
M.Sc
Nikolai Pfender
Patrick Freund Md
Markus Hupp MD PhD
Julien Cohen-adad Md
Objective: Automatic and robust characterization of spinal cord shape from MRI images is relevant to assess the severity of spinal cord comp… (see more)ression in degenerative cervical myelopathy (DCM) and to guide therapeutic strategy. Despite its popularity, the maximum spinal cord compression (MSCC) index has practical limitations to objectively assess the severity of cord compression. Firstly, it is computed by normalizing the anteroposterior cord diameter by that above and below the level of compression, but it does not account for the fact that the spinal cord itself varies in size along the superior-inferior axis, making this MSCC sensitive to the level of compression. Secondly, spinal cord shape varies across individuals, making MSCC also sensitive to the size and shape of every individual. Thirdly, MSCC is typically computed by the expert-rater on a single sagittal slice, which is time-consuming and prone to inter-rater variability. In this study, we propose a fully automatic pipeline to compute MSCC. Methods: We extended the traditional MSCC (based on the anteroposterior diameter) to other shape metrics (transverse diameter, area, eccentricity, and solidity), and proposed a normalization strategy using a database of healthy adults (n=203) to address the variability of the spinal cord anatomy between individuals. We validated the proposed method in a cohort of DCM patients (n=120) with manually derived morphometric measures and predicted the therapeutic decision (operative/conservative) using a stepwise binary logistic regression including demographics, clinical scores, and electrophysiological assessment. Results: The automatic and normalized MSCC measures significantly correlated with clinical scores and predicted the therapeutic decision with higher accuracy than the manual MSCC. Results show that the sensory dysfunction of the upper extremities (mJOA subscore), the presence of myelopathy and the proposed MRI-based normalized morphometric measures were significant predictors of the therapeutic decision. The model yielded an area under the curve of the receiver operating characteristic of 80%. Conclusion: The study introduced an automatic method for computation of normalized MSCC measures of cord compression from MRI scans, which is an important step towards better informed therapeutic decisions in DCM patients. The method is open-source and available in the Spinal Cord Toolbox v6.0.
2024-03-15
medRxiv (preprint)
doi.org
A database of the healthy human spinal cord morphometry in the PAM50 template space
Jan Valošek
Sandrine Bédard
Miloš Keřkovský
Tomáš Rohan
Julien Cohen-Adad
Abstract Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of … (see more)spinal cord 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.
2024-02-02
Imaging Neuroscience (published)
doi.org
Automatic Segmentation of the Spinal Cord Nerve Rootlets
Jan Valošek
Theo Mathieu
Raphaëlle Schlienger
Olivia S. Kowalczyk
Julien Cohen-Adad
Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal co… (see more)rd. 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 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 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 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
2024-02-01
ArXiv (preprint)
doi.org
arxiv.org
SCIseg: Automatic Segmentation of T2-weighted Hyperintense Lesions in Spinal Cord Injury
Enamundram Naga Karthik
Jan Valošek
Andrew C. Smith
Dario Pfyffer
Simon Schading-Sassenhausen
Lynn Farner
Kenneth A. Weber
Patrick Freund
Julien Cohen-Adad
Background: Quantitative MRI biomarkers in spinal cord injury (SCI) can help understand the extent of the focal injury. However, due to the … (see more)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.
2024-01-03
medRxiv (preprint)
doi.org
Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox.
Jan Valošek
Julien Cohen-Adad
2024-01-01
Magnetic Resonance in Medical Sciences (published)
doi.org
Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter
Julien Cohen-Adad
Eva Alonso‐Ortiz
Stephanie Alley
M. M. Laganá
Francesca Baglio
S. Vannesjo
Haleh Karbasforoushan
Maryam Seif
A. Seifert
Junqian Xu
Joo-won Kim
René Labounek
Lubomír Vojtíšek
Marek Dostál
Jan Valošek
Rebecca Sara Samson
Francesco Grussu
Marco Battiston
C. G. Gandini Wheeler-Kingshott
Marios C. Yiannakas … (see 4 more)
Guillaume Gilbert
Torben Schneider
Brian Johnson
Ferran Prados
Spinal cord gray‐matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare… (see more) various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter.
2022-04-05
Magnetic Resonance in Medicine (published)
doi.org
Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter
Julien Cohen-Adad
Eva Alonso‐Ortiz
Stephanie Alley
Maria Marcella Lagana
Francesca Baglio
Signe Johanna Vannesjo
Haleh Karbasforoushan
Maryam Seif
Alan C. Seifert
Junqian Xu
Joo‐Won Kim
René Labounek
Lubomír Vojtíšek
Marek Dostál
Jan Valošek
Rebecca S. Samson
Francesco Grussu
Marco Battiston
Claudia A. M. Gandini Wheeler-Kingshott
Marios C. Yiannakas … (see 4 more)
Guillaume Gilbert
Torben Schneider
Brian Johnson
Ferran Prados
Spinal cord gray‐matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare… (see more) various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter.
2022-04-05
Magnetic Resonance in Medicine (published)
doi.org