Towards contrast-agnostic soft segmentation of the spinal cord
Sandrine Bédard Naga Karthik Enamundram Charidimos Tsagkas Emanuele Pravatà Cristina Granziera Andrew Smith Kenneth Arnold Weber II Julien Cohen-Adad
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 a regression-based loss function, were
then used to train a UNet model for spinal cord segmentation. We evaluated our
model against state-of-the-art methods and performed ablation studies involving
different GT mask types, loss functions, and contrast-specific models. Our
results show that using the soft average 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 contrast-specific methods amongst unseen datasets, vendors,
contrasts, and pathologies (compression, lesions), while accounting for partial
volume effects.