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SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans

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arxiv 2205.01683 v1 pith:VV7GFWPM submitted 2022-05-03 eess.IV cs.CV

Rhydian Windsor , Amir Jamaludin , Timor Kadir , Andrew Zisserman This is my paper · ORCID
classification eess.IVcs.CV
keywords gradingscansradiologicalrangespinenetv2acrossautomatedclinical
verification ladder T0 review T1 audit T2 compute T3 formal
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This technical report presents SpineNetV2, an automated tool which: (i) detects and labels vertebral bodies in clinical spinal magnetic resonance (MR) scans across a range of commonly used sequences; and (ii) performs radiological grading of lumbar intervertebral discs in T2-weighted scans for a range of common degenerative changes. SpineNetV2 improves over the original SpineNet software in two ways: (1) The vertebral body detection stage is significantly faster, more accurate and works across a range of fields-of-view (as opposed to just lumbar scans). (2) Radiological grading adopts a more powerful architecture, adding several new grading schemes without loss in performance. A demo of the software is available at the project website: http://zeus.robots.ox.ac.uk/spinenet2/.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Segmentation Pre-training for Label-Efficient Lumbar Spine Degeneration Grading

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Segmentation pre-training on automatically generated MRI masks lets a spine-grading model reach near full-supervision performance with only 20% of manual grading labels.

  2. Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Pairwise ranking on Genodisc yields continuous spine-degeneration scores that match classification accuracy when discretized and reduce distant-grade errors across 11 MRI grading tasks.

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