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Automatic spondylolisthesis grading from MRIs across modalities using faster adversarial recognition network.

Shen Zhao1, Xi Wu2, Bo Chen1

  • 1University of Western Ontario, London, ON, Canada.

Medical Image Analysis
|August 2, 2019
PubMed
Summary

We developed a Faster Adversarial Recognition (FAR) network for accurate spondylolisthesis grading from MRI scans. This AI model excels at detecting critical vertebrae, improving diagnostic accuracy comparable to physicians.

Keywords:
GAN (generative adversarial network)MRIs across modalitiesObject detectionSpondylolisthesis grading

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Spinal Diagnostics

Background:

  • Grading spondylolisthesis from MRI images is challenging due to difficulties in detecting critical vertebrae and landmarks across varying image characteristics.
  • Existing methods often require precise landmark localization, which is complex and prone to errors in diverse MRI modalities.
  • Accurate spondylolisthesis grading is crucial for effective clinical diagnosis and treatment planning.

Purpose of the Study:

  • To propose and evaluate a novel Faster Adversarial Recognition (FAR) network for accurate spondylolisthesis grading using MRI images.
  • To develop a method that excels at critical vertebrae detection without the need for landmark localization.
  • To assess the robustness and accuracy of the proposed network across different MRI modalities.

Main Methods:

  • Introduced the Faster Adversarial Recognition (FAR) network, employing an adversarial scheme with a multi-task recognition network as the generator and an adversarial module as the discriminator.
  • The generator network performs multi-scale hierarchical feature learning, critical vertebrae detection, classification, bounding box regression, and grading in a hybrid supervised manner.
  • The discriminator module supervises the generator using high-order statistics of detected bounding box coordinates.

Main Results:

  • The FAR network achieved high accuracy in spondylolisthesis grading, with training accuracy of 0.9883 ± 0.0094 and testing accuracy of 0.8933 ± 0.0276.
  • Excellent critical vertebrae detection performance was observed, with detection mAP75 of 1 ± 0 for training and 0.9636 ± 0.0180 for testing.
  • The method demonstrated robustness across different MRI modalities and achieved accuracy comparable to physicians, outperforming state-of-the-art methods.

Conclusions:

  • The Faster Adversarial Recognition (FAR) network provides an accurate and robust solution for spondylolisthesis grading from MRI images.
  • The proposed method effectively detects critical vertebrae, eliminating the need for landmark localization and enhancing diagnostic capabilities.
  • The FAR network shows significant potential for integration into clinical diagnosis workflows for improved spondylolisthesis assessment.