Spine intervertebral disc labeling using a fully convolutional redundant counting modelDownload PDF

25 Jan 2020 (modified: 05 May 2023)Submitted to MIDL 2020Readers: Everyone
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].
6 Replies