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Sandrine Bédard

Alumni

Publications

Contrast-agnostic Spinal Cord Segmentation: A Comparative Study of ConvNets and Vision Transformers
Enamundram Naga Karthik
Sandrine Bédard
Jan Valosek
A. Chandar
Julien Cohen-Adad
The cross-sectional area (CSA) of the spinal cord (SC) computed from its segmentation is a relevant clinical biomarker for the diagnosis and… (see more) monitoring of cord compression and atrophy. One key limitation of existing automatic methods is that their SC segmentations depend on the MRI contrast, resulting in different CSA across contrasts. Furthermore, these methods rely on CNNs, leaving a gap in the literature for exploring the performance of modern deep learning (DL) architectures. In this study, we extend our recent work \cite{Bdard2023TowardsCS} by evaluating the contrast-agnostic SC segmentation capabilities of different classes of DL architectures, namely, ConvNeXt, vision transformers (ViTs), and hierarchical ViTs. We compared 7 different DL models using the open-source \textit{Spine Generic} Database of healthy participants
2024-04-26
MIDL.io/2024/Short_Papers (accepted)
openreview.net
openreview.net
Normalizing Spinal Cord Compression Morphometric Measures: Application in Degenerative Cervical Myelopathy
Sandrine Bédard
Jan Valosek
Maryam Seif
Armin Curt
Simon Schadings
M.Sc
Nikolai Pfender
Patrick Freund
Markus Hupp
Julien Cohen-Adad
The study introduced an automatic method for computation of normalized MSCC measures of cord compression from MRI scans, which is an importa… (see more)nt step towards better informed therapeutic decisions in DCM patients. The method is open-source and available in the Spinal Cord Toolbox v6.0.
2024-03-14
medRxiv (preprint)
doi.org
A database of the healthy human spinal cord morphometry in the PAM50 template space
Jan Valosek
Sandrine Bédard
Miloš Keřkovský
Tomáš Rohan
Julien Cohen-Adad
Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of spinal co… (see more)rd 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.
2024-01-09
Imaging Neuroscience (published)
doi.org
Pontomedullary junction as a reference for spinal cord cross-sectional area: validation across neck positions
Sandrine Bédard
Maxime Bouthillier
Julien Cohen-Adad
Spinal cord cross-sectional area (CSA) is an important MRI biomarker to assess spinal cord atrophy in various neurodegenerative and traumati… (see more)c spinal cord diseases. However, the conventional method of computing CSA based on vertebral levels is inherently flawed, as the prediction of spinal levels from vertebral levels lacks reliability, leading to considerable variability in CSA measurements. Computing CSA from an intrinsic neuroanatomical reference, the pontomedullary junction (PMJ), has been proposed in previous work to overcome limitations associated with using a vertebral reference. However, the validation of this alternative approach, along with its variability across and within participants under variable neck extensions, remains unexplored. The goal of this study was to determine if the variability of CSA across neck flexions/extensions is reduced when using the PMJ, compared to vertebral levels. Ten participants underwent a 3T MRI T2w isotropic scan at 0.6 mm3 for 3 neck positions: extension, neutral and flexion. Spinal cord segmentation, vertebral labeling, PMJ labeling, and CSA were computed automatically while spinal segments were labeled manually. Mean coefficient of variation for CSA across neck positions was 3.99 ± 2.96% for the PMJ method vs. 4.02 ± 3.01% for manual spinal segment method vs. 4.46 ± 3.10% for the disc method. These differences were not statistically significant. The PMJ method was slightly more reliable than the disc-based method to compute CSA at specific spinal segments, although the difference was not statistically significant. This suggests that the PMJ can serve as a valuable alternative and reliable method for estimating CSA when a disc-based approach is challenging or not feasible, such as in cases involving fused discs in individuals with spinal cord injuries.
2023-08-18
Scientific Reports (published)
doi.org
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… (see more)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 (published)
doi.org