University of Oulu

N. Kowlagi et al., "A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine Mri Using Deep Learning," 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp. 1-5, doi: 10.1109/ISBI53787.2023.10230814.

A stronger baseline for automatic Pfirrmann grading of lumbar spine MRI using deep learning

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Author: Kowlagi, Narasimharao1; Nguyen, Huy Hoang1; McSweeney, Terence1;
Organizations: 1University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-10-04


This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning. Such a method is essential for the automatic quantification of structural changes in the spine, which is valuable for understanding low back pain. Multiple recent studies investigated different architecture designs, and the most recent success has been attributed to the use of transformer architectures. In this work, we argue that with a well-tuned three-stage pipeline comprising semantic segmentation, localization, and classification, convolutional networks outperform the state-of-the-art approaches. We conducted an ablation study of the existing methods in a population cohort, and report performance generalization across various subgroups. Our code is publicly available to advance research on disc degeneration and low back pain.

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Series: IEEE International Symposium on Biomedical Imaging
ISSN: 1945-7928
ISSN-E: 1945-8452
ISSN-L: 1945-7928
ISBN: 978-1-6654-7358-3
ISBN Print: 978-1-6654-7359-0
Article number: 10230814
DOI: 10.1109/isbi53787.2023.10230814
Host publication: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
Conference: IEEE International Symposium on Biomedical Imaging
Type of Publication: A4 Article in conference proceedings
Field of Science: 217 Medical engineering
Funding: This work was funded by the Marie Sklodowska-Curie Actions (MCSA), International Training Network, under grant agreement 955735 for the Disc4All consortium. This work was also supported by funding from the Academy of Finland (Profi6 336449 funding program).
EU Grant Number: (955735) Disc4All - Training network to advance integrated computational simulations in translational medicine, applied to intervertebral disc degeneration
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