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Related Concept Videos

General Structure of a Vertebra01:30

General Structure of a Vertebra

A typical vertebra, with the exception of the sacrum and coccyx, consists of a body, a vertebral arch, and seven different projections termed processes. The anterior portion of the vertebrae, the body, supports about half the body’s weight. The vertebral bodies progressively increase in size and thickness from the cervical region to the lumbar region of the vertebral column. The intervertebral discs present between the bodies of adjacent vertebrae firmly unites them, forming a continuous column.

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Precision Measurements and Parametric Models of Vertebral Endplates
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Semantic edge-guided single-view 2D/3D registration for vertebrae in X-rays.

Ao Shen1, Junfeng Jiang2, Ye Tang1

  • 1College of Information Science and Engineering, Hohai University, Changzhou, Jiangsu, China.

Medical Physics
|March 15, 2026
PubMed
Summary

This study introduces ESegMamba, a novel AI framework for accurate 2D/3D lumbar spine registration using vertebral body edges. The method achieves high precision and efficiency for intraoperative spinal navigation.

Keywords:
2D/3D RegistrationEdge‐guidedSegMamba

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Spinal Surgery

Background:

  • AI integration in image-guided interventions aims to extract 3D information from 2D imaging.
  • Current 2D/3D registration methods struggle with image domain gaps and feature extraction, leading to reduced accuracy.
  • Inadequate initial poses and local optima in fine registration limit precision in existing techniques.

Purpose of the Study:

  • Develop a robust single-view lumbar spine 2D/3D registration framework.
  • Align preoperative CT scans with intraoperative X-rays for enhanced accuracy and efficiency.
  • Address limitations of current methods in clinical intraoperative settings.

Main Methods:

  • Utilize vertebral body edges in X-rays as novel semantic features for guiding 2D/3D registration.
  • Employ ESegMamba, an efficient U-shaped Mamba network with GHPA and GAC modules, for robust edge extraction.
  • Validate ESegMamba against SegMamba, SwinUNETR, and UNETR; compare registration performance with landmark-based, intensity-based, and learning-based methods using mTRE.

Main Results:

  • ESegMamba achieved 90.36% Dice and 85.49% mIoU, outperforming other networks with fewer parameters.
  • Demonstrated significant Dice improvement over SegMamba (Cohen's d = 2.05, p < 0.00067).
  • Achieved superior 2D/3D registration performance, with large practical improvements in mTRE compared to Xreg and PSSS (p < 0.0011).

Conclusions:

  • The ESegMamba-powered method shows statistically significant improvements over intensity-based benchmarks.
  • Achieved sub-2mm accuracy and ~10s processing time on clinical data, confirming efficacy for intraoperative spinal navigation.
  • Code is publicly available, facilitating further research and application.