  Spinal cord injury (SCI) is a devastating incidence leading to permanent
paralysis and loss of sensory-motor functions potentially resulting in the
formation of lesions within the spinal cord. Imaging biomarkers obtained from
magnetic resonance imaging (MRI) scans can predict the functional recovery of
individuals with SCI and help choose the optimal treatment strategy. Currently,
most studies employ manual quantification of these MRI-derived biomarkers,
which is a subjective and tedious task. In this work, we propose (i) a
universal tool for the automatic segmentation of intramedullary SCI lesions,
dubbed \texttt{SCIsegV2}, and (ii) a method to automatically compute the width
of the tissue bridges from the segmented lesion. Tissue bridges represent the
spared spinal tissue adjacent to the lesion, which is associated with
functional recovery in SCI patients. The tool was trained and validated on a
heterogeneous dataset from 7 sites comprising patients from different SCI
phases (acute, sub-acute, and chronic) and etiologies (traumatic SCI, ischemic
SCI, and degenerative cervical myelopathy). Tissue bridges quantified
automatically did not significantly differ from those computed manually,
suggesting that the proposed automatic tool can be used to derive relevant MRI
biomarkers. \texttt{SCIsegV2} and the automatic tissue bridges computation are
open-source and available in Spinal Cord Toolbox (v6.4 and above) via the
\texttt{sct\_deepseg -task seg\_sc\_lesion\_t2w\_sci} and
\texttt{sct\_analyze\_lesion} functions, respectively.
