# Spine intervertebral disc labeling using a fully convolutional redundant counting model

arXiv: https://arxiv.org/abs/2003.04387 · HTML: https://mlsanity.com/paper/2003.04387/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling](https://mlsanity.com/paper/2108.06554/) (2021)
- [Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach](https://mlsanity.com/paper/2204.02943/) (2022)
- [HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling](https://mlsanity.com/paper/2311.12486/) (2023)
- [Automatic Segmentation of the Spinal Cord Nerve Rootlets](https://mlsanity.com/paper/2402.00724/) (2024)

## Abstract (arXiv)

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.