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

Jesse A.M. van Doormaal, Tim Fick, Meedie Ali, Mare Köllen, Vince van der Kuijp, Tristan P.C. van Doormaal

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Objective: Effective image segmentation of cerebral structures is fundamental to 3-dimensional techniques such as augmented reality. To be clinically viable, segmentation algorithms should be fully automatic and easily integrated in existing digital infrastructure. We created a fully automatic adaptive-meshing-based segmentation system for T1-weighted magnetic resonance images (MRI) to automatically segment the complete ventricular system, running in a cloud-based environment that can be accessed on an augmented reality device. This study aims to assess the accuracy and segmentation time of the system by comparing it to a manually segmented ground truth dataset. Methods: A ground truth (GT) dataset of 46 contrast-enhanced and non–contrast-enhanced T1-weighted MRI scans was manually segmented. These scans also were uploaded to our system to create a machine-segmented (MS) dataset. The GT data were compared with the MS data using the Sørensen–Dice similarity coefficient and 95% Hausdorff distance to determine segmentation accuracy. Furthermore, segmentation times for all GT and MS segmentations were measured. Results: Automatic segmentation was successful for 45 (98%) of 46 cases. Mean Sørensen–Dice similarity coefficient score was 0.83 (standard deviation [SD] = 0.08) and mean 95% Hausdorff distance was 19.06 mm (SD = 11.20). Segmentation time was significantly longer for the GT group (mean = 14405 seconds, SD = 7089) when compared with the MS group (mean = 1275 seconds, SD = 714) with a mean difference of 13,130 seconds (95% confidence interval 10,130–16,130). Conclusions: The described adaptive meshing-based segmentation algorithm provides accurate and time-efficient automatic segmentation of the ventricular system from T1 MRI scans and direct visualization of the rendered surface models in augmented reality.

Original languageEnglish
Pages (from-to)e9-e24
JournalWorld Neurosurgery
Publication statusPublished - Dec 2021


  • Augmented reality
  • Image segmentation
  • Ventricular system


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