mlsanityData to 7 Oct 2026

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

arXiv 2205.01683 ↗First posted 3 May 2022eess.IV cs.CVcited by 7

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Abstract (arXiv)

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/.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

6 of 17 references.

References · 6

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2014Return of the Devil in the Details: Delving Deep into Convolutional Netscited by 122
  4. 2020A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRIcited by 12
  5. 2017Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularizationcited by 11
  6. 2020The Ladder Algorithm: Finding Repetitive Structures in Medical Images by Inductioncited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 1; 2026: 4.

7 papers.

  1. 2022Context-Aware Transformers For Spinal Cancer Detection and Radiological Gradingcited by 8
  2. 2022A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine MRI Using Deep Learningcited by 3
  3. 2025M-SCAN: A Multistage Framework for Lumbar Spinal Canal Stenosis Grading Using Multi-View Cross Attentioncited by 2
  4. 2026VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequencescited by 1
  5. 2026Video-Text Temporal Localization via Multi-Scale Convolution and Dynamic Routingcited by 0
  6. 2026Segmentation Pre-training for Label-Efficient Lumbar Spine Degeneration Gradingcited by 0
  7. 2026Beyond Fluency: A Clinical Benchmark and Anomaly-Enhanced Baseline for Spine MRI Report Generationcited by 0