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Labeling intervertebral discs is relevant as it notably enables clinicians tounderstand the relationship between a patient's symptoms (pain, paralysis) andthe exact level of spinal cord injury. However manually labeling those discs isa tedious and user-biased task which would benefit from automated methods.While some automated methods already exist for MRI and CT-scan, they are eithernot publicly available, or fail to generalize across various imaging contrasts.In this paper we combine a Fully Convolutional Network (FCN) with inceptionmodules to localize and label intervertebral discs. We demonstrate aproof-of-concept application in a publicly-available multi-center andmulti-contrast MRI database (n=235 subjects). The code is publicly available athttps://github.com/neuropoly/vertebral-labeling-deep-learning.

Spine intervertebral disc labeling using a fully convolutional redundant counting model