Keywords: Spinal Cord, Spinal Cord, Spinal Cord Injury, Morphometry, Analysis Methods
Motivation: Manual measures of spinal lesion morphometry from MRI scans correlate with neurological prognosis in spinal cord injury (SCI) patients but are prone to intra- and inter-rater variability.
Goal(s): Develop a software solution that automates the measurements of lesion morphometry.
Approach: We developed a deep learning model that segments the spinal cord and intramedullary lesions, and that computes lesion length and width on the midsagittal slice. This method was compared against manual measurements in an SCI cohort.
Results: The automatic approach showed good agreement with manual measurements. The method is open-source and will be released as part of the Spinal Cord Toolbox (v6.5+).
Impact: Automatic computation of lesion morphometry can replace manual measurements, thus facilitating large multi-center studies in spinal cord injury patients by reducing intra- and inter-expert variability and saving time.
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.
Keywords