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Fully automatic brain tumor segmentation for 3D evaluation in augmented reality.

Tim Fick1, Jesse A M van Doormaal2, Lazar Tosic3

  • 11Department of Neuro-oncology, Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.

Neurosurgical Focus
|August 1, 2021
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Summary
This summary is machine-generated.

This study introduces an automatic segmentation algorithm for creating 3D models from MRI scans, significantly speeding up augmented reality workflows for brain tumor evaluation. The algorithm proves reliable and accurate for clinical use.

Keywords:
augmented realitybrain tumorsegmentation algorithm

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Neurosurgery

Background:

  • Current augmented reality (AR) workflows require time-consuming manual or semi-automatic 3D model segmentation.
  • Developing automated methods is crucial for efficient clinical applications.

Purpose of the Study:

  • To develop and validate an automatic segmentation algorithm for generating 3D models from T1-weighted MRI sequences.
  • To integrate this algorithm into an automated cloud-based workflow for AR-based 3D evaluation of anatomical structures, specifically contrast-enhancing brain tumors.

Main Methods:

  • An automatic segmentation algorithm was developed to generate 3D models from single T1-weighted MRI sequences.
  • Fifty contrast-enhanced T1-weighted MRI scans of patients with contrast-enhancing lesions (≥5 cm³) were processed.
  • The algorithm's accuracy was compared against manual segmentation using Sørensen-Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and 95th percentile of Hausdorff distance (HD95).

Main Results:

  • The automatic segmentation algorithm achieved a mean computation time of 753 ± 128 seconds.
  • Performance metrics included a mean DSC of 0.868 ± 0.07, mean ASSD of 1.31 ± 0.63 mm, and mean HD95 of 4.80 ± 3.18 mm.
  • Higher accuracy was observed for meningiomas compared to metastases, and for supratentorial compared to infratentorial metastases.

Conclusions:

  • The developed automatic cloud-based segmentation algorithm is reliable, accurate, and efficient for neurosurgeons.
  • It facilitates 3D AR visualization of contrast-enhancing intracranial lesions, aiding in clinical practice.
  • Future work includes incorporating additional MRI sequences and refining accuracy for broader AR workflow applications.