Abstract #1395
Automatic segmentation of spinal cord multiple sclerosis lesions across multiple sites, contrasts and vendors
Pierre-Louis Benveniste1,2, Jan Valošek1,2,3,4, Michelle Chen1, Nathan Molinier1,2, Lisa Eunyoung Lee5,6, Alexandre Prat7,8, Zachary Vavasour9, Roger Tam9, Anthony Traboulsee10, Shannon Kolind10, Jiwon Oh5,6, and Julien Cohen-Adad1,2,11,12
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montréal, QC, Canada, 2Mila - Quebec AI Institute, Montréal, QC, Canada, 3Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic, 4Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic, 5Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada, 6BARLO Multiple Sclerosis Centre & Keenan Research Centre, St. Michael's Hospital, Toronto, ON, Canada, 7Department of neuroscience, Université de Montréal, Montréal, QC, Canada, 8Neuroimmunology research laboratory, University of Montreal Hospital Research Centre (CRCHUM), Montréal, QC, Canada, 9School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada, 10Departments of Medicine (Neurology), Physics, Radiology, University of British Columbia,, Vancouver, BC, Canada, 11Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada, 12Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, QC, Canada
Synopsis
Keywords: Diagnosis/Prediction, Multiple Sclerosis, Deep Learning, Segmentation, Spinal Cord
Motivation: Longitudinal analysis of spinal cord multiple sclerosis (MS) lesions is clinically relevant for the early diagnosis and monitoring of MS progression.
Goal(s): Develop a deep learning tool for the automatic segmentation of MS spinal cord lesions on PSIR and STIR images from multiple sites.
Approach: A nnUNet model was trained and tested on the baseline data and applied to follow-up scans to create lesion distribution maps.
Results: We demonstrated the utility of the model to map the spatio-temporal distribution of MS lesions across MS phenotypes. The model is packaged into an open-source software.
Impact: Automatic segmentation of spinal cord lesions in large cohorts helps to identify signatures of MS phenotypes for ultimately improving prognosis and optimizing treatment for people with MS.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Click here for more information on becoming a member.
