A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine MRI
Using Deep Learning
- URL: http://arxiv.org/abs/2210.14597v1
- Date: Wed, 26 Oct 2022 10:12:21 GMT
- Title: A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine MRI
Using Deep Learning
- Authors: Narasimharao Kowlagi, Huy Hoang Nguyen, Terence McSweeney, Simo
Saarakkala, Juhani m\"a\"att\"a, Jaro Karppinen, Aleksei Tiulpin
- Abstract summary: This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning.
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.
Our code is publicly available to advance research on disc degeneration and low back pain.
- Score: 2.724641898087941
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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