SCIseg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury.

Enamundram Naga Karthik Jan Valošek Andrew C Smith Dario Pfyffer Simon Schading-Sassenhausen Lynn Farner Kenneth A Webber Patrick Freund Julien Cohen-Adad

Published in: medRxiv : the preprint server for health sciences (2024)

Automatic segmentation of intramedullary lesions commonly seen in SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model segments lesions across different etiologies, scanner manufacturers, and heterogeneous image resolutions. SCIseg is open-source and accessible through the Spinal Cord Toolbox.

Keyphrases

deep learning
spinal cord injury
spinal cord
convolutional neural network
neuropathic pain
machine learning
magnetic resonance
single cell