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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2205.01683 (eess)
[Submitted on 3 May 2022]

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

Authors:Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman
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Abstract: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: this http URL.
Comments: Technical Report, 22 pages, 9 Figures
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2205.01683 [eess.IV]
  (or arXiv:2205.01683v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2205.01683

Submission history

From: Rhydian Windsor [view email]
[v1] Tue, 3 May 2022 15:05:58 UTC (12,948 KB)
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