VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI

Background Objective and volumetric quantification is a necessary step in the assessment and comparison of endolymphatic hydrops (ELH) results. Here, we introduce a novel tool for automatic volumetric segmentation of the endolymphatic space (ELS) for ELH detection in delayed intravenous gadolinium-e...

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Bibliographic Details
Published in:Journal of neurology Vol. 267; no. Suppl 1; pp. 185 - 196
Main Authors: Gerb, J., Ahmadi, S. A., Kierig, E., Ertl-Wagner, B., Dieterich, M., Kirsch, V.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020
Springer Nature B.V
Subjects:
Age
Algorithms
Datasets
Edema
Etiology
Gadolinium
Inner ear
Intravenous administration
Magnetic resonance imaging
Medicine
Medicine & Public Health
Neurology
Neuroradiology
Neurosciences
Original Communication
Segmentation
Vertigo
Intravenous application
Automatic segmentation
Volumetric
MRI
Local thresholding
Endolymphatic hydrops
Contrast agent
Inner ear
ISSN:0340-5354, 1432-1459, 1432-1459
Online Access:Get full text
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Summary:Background Objective and volumetric quantification is a necessary step in the assessment and comparison of endolymphatic hydrops (ELH) results. Here, we introduce a novel tool for automatic volumetric segmentation of the endolymphatic space (ELS) for ELH detection in delayed intravenous gadolinium-enhanced magnetic resonance imaging of inner ear (iMRI) data. Methods The core component is a novel algorithm based on Volumetric Local Thresholding (VOLT). The study included three different data sets: a real-world data set (D1) to develop the novel ELH detection algorithm and two validating data sets, one artificial (D2) and one entirely unseen prospective real-world data set (D3). D1 included 210 inner ears of 105 patients (50 male; mean age 50.4 ± 17.1 years), and D3 included 20 inner ears of 10 patients (5 male; mean age 46.8 ± 14.4 years) with episodic vertigo attacks of different etiology. D1 and D3 did not differ significantly concerning age, gender, the grade of ELH, or data quality. As an artificial data set, D2 provided a known ground truth and consisted of an 8-bit cuboid volume using the same voxel-size and grid as real-world data with different sized cylindrical and cuboid-shaped cutouts (signal) whose grayscale values matched the real-world data set D1 (mean 68.7 ± 7.8; range 48.9–92.8). The evaluation included segmentation accuracy using the Sørensen-Dice overlap coefficient and segmentation precision by comparing the volume of the ELS. Results VOLT resulted in a high level of performance and accuracy in comparison with the respective gold standard. In the case of the artificial data set, VOLT outperformed the gold standard in higher noise levels. Data processing steps are fully automated and run without further user input in less than 60 s. ELS volume measured by automatic segmentation correlated significantly with the clinical grading of the ELS ( p  < 0.01). Conclusion VOLT enables an open-source reproducible, reliable, and automatic volumetric quantification of the inner ears’ fluid space using MR volumetric assessment of endolymphatic hydrops. This tool constitutes an important step towards comparable and systematic big data analyses of the ELS in patients with the frequent syndrome of episodic vertigo attacks. A generic version of our three-dimensional thresholding algorithm has been made available to the scientific community via GitHub as an ImageJ-plugin.
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ISSN:0340-5354
1432-1459
1432-1459
DOI:10.1007/s00415-020-10062-8