Spine intervertebral disc labeling using a fully convolutional redundant counting model

Lucas Rouhier, Francisco Perdigon Romero, Joseph Paul Cohen, Julien Cohen-Adad

Keywords: Deep learning, Keypoints detection, Spinal cord, MRI, Intervertebral disc

Track: short paper

Abstract: 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 [URL will be added after the double blind review].

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/spine-intervertebral-disc-labeling-using-a/code)