Abstract #4795

Spinal Cord Grey Matter Segmentation using a Light-Weight Off-The-Shelf Neural Network

Jackie Yik^1,2, Roger Tam^3,4, John K. Kramer^2,5, Cornelia Laule^1,2,4,6, and Hanwen Liu^1,2

¹Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada, ²International Collaboration on Repair Discoveries, Vancouver, BC, Canada, ³School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada, ⁴Radiology, University of British Columbia, Vancouver, BC, Canada, ⁵Kinesiology, University of British Columbia, Vancouver, BC, Canada, ⁶Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada

Spinal cord grey matter segmentation is typically done manually. Automatic segmentation methods exist but are generally highly customized. We used an off-the-shelf neural network (LinkNet) to segment the grey matter in the spinal cord to assess the performance of a method with a generic architecture, which may be easier to replicate on different machine learning frameworks. Manual segmentation was used as training data. The performance of our trained network was compared to an automatic segmentation method in the Spinal Cord Toolbox (SCT), and both networks produced similar results, demonstrating the viability of the off-the-shelf approach.

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