Spine intervertebral disc labeling using a fully convolutional redundant counting model
This work addresses a tedious and user-biased task for clinicians in diagnosing spinal cord injuries, but it is incremental as it builds on existing fully convolutional networks.
The paper tackles the problem of automating the labeling of intervertebral discs in spinal MRI to reduce manual effort and bias, achieving a proof-of-concept application on a multi-center database with 235 subjects.
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.