SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans
This is an incremental improvement for clinicians and researchers needing efficient spinal image analysis.
The authors tackled automated detection, labeling, and radiological grading of vertebral bodies in spinal MR scans, resulting in SpineNetV2, which is faster, more accurate, and works across more scan types than its predecessor.
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/.