1. Fully Automatic Adaptive Meshing Based Segmentation of the Ventricular System for Augmented Reality Visualization and Navigation.
- Author
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van Doormaal JAM, Fick T, Ali M, Köllen M, van der Kuijp V, and van Doormaal TPC
- Subjects
- Databases, Factual, Humans, Imaging, Three-Dimensional trends, Magnetic Resonance Imaging trends, Neuronavigation trends, Prospective Studies, Registries, Augmented Reality, Cerebral Ventricles diagnostic imaging, Imaging, Three-Dimensional methods, Magnetic Resonance Imaging methods, Neuronavigation methods
- Abstract
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., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2021
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