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Fully Automatic Adaptive Meshing Based Segmentation of the Ventricular System for Augmented Reality Visualization and

Jesse A M van Doormaal1, Tim Fick2, Meedie Ali3

  • 1Department of Neurosurgery, University Medical Center Utrecht, Utrecht, Province of Utrecht, the Netherlands.

World Neurosurgery
|August 1, 2021
PubMed
Summary

This study presents an automatic adaptive meshing system for segmenting cerebral ventricles in T1 MRI scans. The system achieves high accuracy and significantly reduces segmentation time compared to manual methods.

Keywords:
Augmented realityImage segmentationVentricular system

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Neurosurgery

Background:

  • Accurate segmentation of cerebral structures is crucial for 3D techniques like augmented reality.
  • Clinical viability requires fully automatic segmentation algorithms integrated into existing digital infrastructure.

Purpose of the Study:

  • To develop and assess a fully automatic adaptive-meshing-based segmentation system for T1-weighted MRI scans.
  • To evaluate the system's accuracy and segmentation time for the complete ventricular system.

Main Methods:

  • A ground truth dataset of 46 T1-weighted MRI scans was manually segmented.
  • The adaptive meshing system automatically segmented the same scans.
  • Segmentation accuracy was compared using Sørensen-Dice similarity coefficient and 95% Hausdorff distance.

Main Results:

  • Automatic segmentation achieved 98% success rate (45/46 cases).
  • Mean Sørensen-Dice similarity was 0.83 (SD=0.08), and mean 95% Hausdorff distance was 19.06 mm (SD=11.20).
  • Automatic segmentation time (1275s) was significantly faster than manual segmentation (14405s).

Conclusions:

  • The adaptive meshing algorithm provides accurate and time-efficient automatic segmentation of the ventricular system from T1 MRI.
  • The system enables direct visualization of rendered surface models in augmented reality.