  Labeling intervertebral discs is relevant as it notably enables clinicians to
understand the relationship between a patient's symptoms (pain, paralysis) and
the exact level of spinal cord injury. However manually labeling those discs is
a tedious and user-biased task which would benefit from automated methods.
While some automated methods already exist for MRI and CT-scan, they are either
not publicly available, or fail to generalize across various imaging contrasts.
In this paper we combine a Fully Convolutional Network (FCN) with inception
modules to localize and label intervertebral discs. We demonstrate a
proof-of-concept application in a publicly-available multi-center and
multi-contrast MRI database (n=235 subjects). The code is publicly available at
https://github.com/neuropoly/vertebral-labeling-deep-learning.
