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arXiv · 2003.04387

Spine intervertebral disc labeling using a fully convolutional redundant counting model

Lucas Rouhier·Francisco Perdigon Romero·Joseph Paul Cohen·Julien Cohen-Adad
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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 https://github.com/neuropoly/vertebral-labeling-deep-learning.

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Lucas Rouhier, Francisco Perdigon Romero, Joseph Paul Cohen, Julien Cohen-Adad. 2020-03-09. Spine intervertebral disc labeling using a fully convolutional redundant counting model. https://arxiv.org/abs/2003.04387

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