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

@article{Windsor2022SpineNetV2AD,
  title={SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans},
  author={Rhydian Windsor and Amir Jamaludin and Timor Kadir and Andrew Zisserman},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.01683}
}
This technical paper 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 sigificantly faster, more accurate and works across a… 
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References

SHOWING 1-10 OF 16 REFERENCES
SpineNet: Automatically Pinpointing Classification Evidence in Spinal MRIs
TLDR
A Convolutional Neural Network architecture and training scheme to predict multiple radiological scores on multiple slice sagittal MRIs and the prediction of a heat-map of evidence hotspots for each score is described.
SpineNet: Automated classification and evidence visualization in spinal MRIs
Vertebrae Detection and Labelling in Lumbar MR Images
TLDR
It is shown that marrying two strong algorithms, the DPM object detector of Felzenszwalb et al. and inference using dynamic programming on chains, results in a simple, computationally cheap procedure, that achieves state-of-the-art performance.
ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist
TLDR
Automation of radiological grading is now on par with human performance and can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis.
Robust Detection and Segmentation for Diagnosis of Vertebral Diseases Using Routine MR Images
TLDR
The main advantages of the method are high speed, robust handling of a large variety of routine clinical images, and simple and minimal user interaction.
A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI
TLDR
A novel convolutional method is proposed using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies and Convolutional image-to-image translation followed by beam search to label vertebral levels in a self-consistent manner for whole spine MRIs.
Radiological Grading of Spinal MRI
TLDR
This paper describes a fully automatic system for obtaining the standard Pfirrmann degeneration grading of individual intervertebral spinal discs in T2 MRI scans and proposes new image features that outperform previous features and utilizes techniques to improve robustness to MR imaging variations.
Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data
TLDR
Results show that clinically annotated image data from one image archive is sufficient to train a deep learning-based pipeline for accurate detection and labeling of MR images depicting the spine, and support using deep learning to assist radiologists in their work by providing highly accurate labels that only require rapid confirmation.
Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations
TLDR
A robust localization and identification algorithm which builds upon supervised classification forests and avoids an explicit parametric model of appearance is proposed which outperforms state-of-the-art on pathological cases and overcome the tedious requirement for dense annotations by a semi-automatic labeling strategy.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
TLDR
This paper proposes an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes and outperforms other state-of-the-art methods in terms of localization accuracy.
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