  Spinal cord segmentation is clinically relevant and is notably used to
compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring
of cord compression or neurodegenerative diseases such as multiple sclerosis.
While several semi and automatic methods exist, one key limitation remains: the
segmentation depends on the MRI contrast, resulting in different CSA across
contrasts. This is partly due to the varying appearance of the boundary between
the spinal cord and the cerebrospinal fluid that depends on the sequence and
acquisition parameters. This contrast-sensitive CSA adds variability in
multi-center studies where protocols can vary, reducing the sensitivity to
detect subtle atrophies. Moreover, existing methods enhance the CSA variability
by training one model per contrast, while also producing binary masks that do
not account for partial volume effects. In this work, we present a deep
learning-based method that produces soft segmentations of the spinal cord.
Using the Spine Generic Public Database of healthy participants
($\text{n}=267$; $\text{contrasts}=6$), we first generated participant-wise
soft ground truth (GT) by averaging the binary segmentations across all 6
contrasts. These soft GT, along with aggressive data augmentation and a
regression-based loss function, were used to train a U-Net model for spinal
cord segmentation. We evaluated our model against state-of-the-art methods and
performed ablation studies involving different loss functions and domain
generalization methods. Our results show that using the soft segmentations
along with a regression loss function reduces CSA variability ($p < 0.05$,
Wilcoxon signed-rank test). The proposed spinal cord segmentation model
generalizes better than the state-of-the-art methods amongst unseen datasets,
vendors, contrasts, and pathologies (compression, lesions), while accounting
for partial volume effects.
