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

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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.ResultsVOLT 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).ConclusionVOLT 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.
D2 artificial data set-visualization and results. As an artificial data set, D2 provided a known ground truth to test and compare VOLT cutoff versions to Otsu's method. a A transversal slicewise visualization of D2 in the middle. D2 can be viewed in the very middle and included an 8-bit cuboid volume with different sizes of cylindrical and cuboid-shaped cutouts (signal). To this signal different types of real-world MRI imitating noise were added stepwise in the form of increasing blurriness (Gaussian blur kernel, SD range 1-6 voxel in x/y/z-direction; SD = standard deviations, visualized to the left) and increasing scatter (SD range of intensity variation: 0-50 SD, visualized to the right). b Based on empirical observations in the development data set (D1), VOLT was compared to Otsu's method (O = grey) at three cutoff variations (c6 = forest green, c8 = red, c10 = yellow). Both VOLT cutoff versions and Otsu's method fared better with blurriness noise (x-axis of the left graph) in comparison with scatter noise (x-axis of the right graph). More specifically, VOLT cutoff versions showed a high level of agreement in terms of Dice overlap (y-axis within the graphs) with Otsu's scores in data sets with low noise levels (please compare blurriness 2, framed in mint green and scatter 20, framed in pink). The higher the noise level, the more VOLT cutoff versions outperformed Otsu's method (please note blurriness 5, framed in purple and scatter 50, framed in blue). The corresponding output (c) can easily be compared with the groundtruth by following said color frames. D2 data set 2, c6 cutoff 6, c8 cutoff 8, c10 cutoff 10, O Otsu's method
… 
VOLT flowchart and output examples. The flowchart shows a step-by-step overview of the VOLT processing pipeline of a left inner ear. The different steps correspond to the boxes in a counterclockwise fashion (a, b, c). a Describes data pre-processing, b data processing, and c shows output examples. Within each box, processing steps following orange arrows indicate the order of the main program steps, and green arrows indicate supporting steps. Data pre-processing (a) consists of cropping the inner ear from CISS and FLAIR MR images (only step requiring user input), co-registration, and using a cloud-based deep convolutional neural network (CNN) to create a mask of the inner ear. During data processing, (b) the mask is dilated to include a small seam around the inner ear region-of-interest (ROI). Then, a fusion volume is created, contrast-enhanced, and the fusion volume is 3D reconstructed. VOLT is performed, volumes are reconstructed into a transversal plane and re-sampled into one volume. After 3D blurring, single-voxel noise is removed, and a three-dimensional outline based on the mask is added to the final result. (c) depicts two output examples of the right inner ear. The upper row shows the corresponding cropped FLAIR-MR image; the middle row shows a 2D depiction of the VOLT output, and the lower row shows the 3D visualization of VOLT-output. The inner ear to the left displays no endolymphatic hydrops (ELH). The inner ear to the right displays an ELH grade 2. CISS constructive interference in steady-state, MR magnetic resonance, FLAIR fluid-attenuated inversion recovery, VOLT volumetric local thresholding
… 
D3 prospective validation data set results. D3 was used to validate VOLT on entirely unseen real-world data (20 inner ears). VOLT with the three variations cutoff 6 (c6 = dark green), cutoff 8 (c8 = red), and cutoff 10 (c10 = yellow) were compared to manual (M) segmentation (= grey, that was considered the gold standard). Ear-specific segmentation accuracy was evaluated using the Sørensen-Dice overlap coefficient (DS, upper graph), and segmentation precision were estimated by comparing the volume of the ELS (V, middle graph). Overall, DS of all three VOLT variations was high (c6: 97.0%±\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document} 0.7, c8: 96.6%±\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document} 0.8, c10: 95.9% 97%±\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document} 0.9). The influence of endolymphatic hydrops (ELH = colored light green) and data quality (dQ = colored blue) can easily be seen in the lowest graph. Data quality was defined as mean the greyscale value (or intensity). Note that the grade of ELH correlated significantly with the endolymphatic volume of both the manual segmentation method (p < 0.05) and VOLT cutoff variations c6-8–10 (p < 0.01). c6 cutoff 6, c8 cutoff 8, c10 cutoff 10, D3 data set 3, dQ data quality, DS Dice score, M manual segmentation
… 
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Journal of Neurology (2020) 267 (Suppl 1):S185–S196
https://doi.org/10.1007/s00415-020-10062-8
ORIGINAL COMMUNICATION
VOLT: anovel open‑source pipeline forautomatic segmentation
ofendolymphatic space ininner ear MRI
J.Gerb1,2· S.A.Ahmadi1,3· E.Kierig1,2· B.Ertl‑Wagner4,5· M.Dieterich1,2,3,6· V.Kirsch1,2,3
Received: 7 April 2020 / Revised: 2 June 2020 / Accepted: 6 July 2020 / Published online: 14 July 2020
© The Author(s) 2020
Abstract
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.
Keywords Endolymphatic hydrops· Inner ear· MRI· Intravenous application· Contrast agent· Volumetric· Local
thresholding· Automatic segmentation
* V. Kirsch
valerie.kirsch@med.lmu.de
1 Department ofNeurology, University Hospital, Ludwig-
Maximilians-Universität München, Marchioninistraße 15,
81377Munich, Germany
2 German Center forVertigo andBalance
Disorders – IFB-LMU, University Hospital,
Ludwig-Maximilians-Universität München, Munich,
Germany
3 Graduate School ofSystemic Neuroscience (GSN),
Ludwig-Maximilians-Universität München, Munich,
Germany
4 Department ofRadiology, University Hospital,
Ludwig-Maximilians-Universität München, Munich,
Germany
5 Department ofRadiology, The Hospital forSick Children,
University ofToronto, Toronto, Canada
6 Munich Cluster forSystems Neurology (SyNergy), Munich,
Germany
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S186 Journal of Neurology (2020) 267 (Suppl 1):S185–S196
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Abbreviations
3D Three-dimensional
Bl Blurriness
BPPV Benign paroxysmal positional vertigo
BVP Bilateral vestibulopathy
CISS Constructive interference in steady-state
DS Dice score
DSGZ Interdisciplinary German Center for Vertigo
and Balance Disorders
EF Endolymphatic fluid
ELH Endolymphatic hydrops
ELS Endolymphatic space
FLAIR Fluid-attenuated inversion recovery
GRAPPA Generalized auto-calibrating partially parallel
acquisition
iMRI Delayed intravenous gadolinium-enhanced
magnetic resonance imaging of the inner ear
iv Intravenous
L Left
R Right
MD Menière’s disease
MRI Magnetic resonance imaging
NEF Non–endolymphatic fluid
ROC Receiver operating characteristics
Sc Scatter
SD Standard deviation
SVV Subjective visual vertical
TLS Total fluid space
U Unclear
VE Volume of the endolymphatic space
VT Volume of the total fluid space
vHIT Videooculography during the head-impulse
test
VM Vestibular migraine
VN Vestibular neuritis
VP Vestibular paroxysmia
Introduction
Delayed intravenous gadolinium-enhanced magnetic reso-
nance imaging of the inner ear (iMRI) enables direct, in-
vivo, non-invasive verification of endolymphatic hydrops
(ELH) simultaneously in both inner ears [1]. This reasonably
recent methodical development introduced a broader, more
structured investigation to the clinical syndromes associated
with ELH, which up to then was thought to be pathogno-
monic to Menière’s disease (MD) [2]. Today, the relation-
ship between ELH and MD symptoms (for review cp [3]),
as well as the specificity of ELH for MD, has come under
scrutiny. The underlying reason is that different ELH pat-
terns can be found not only in MD [4, 5], but also so far in
3.3–28% of healthy ears [6, 7], various inner ear [810] and
central [1114] pathologies, as well as in anatomic or vas-
cular abnormalities affecting endolymph resorption [1517].
Because of this, objective and volumetric quantification
is considered a necessary step to assess and compare ELH
results. So far, the clinical gold standard assessment of the
endolymphatic space (ELS) is based on a semi-quantita-
tive and subjective grading reliant on a few MR slices in a
transversal plane. Current ELS MR volumetric assessment
approaches propose either manual or semi-automatic seg-
mentation [18]. Already a considerable improvement, these
approaches lack normalization and require lengthy user
interaction that is not suitable for use in more extensive
group studies or clinical routine.
Here, we introduce a novel tool for automatic volumetric
segmentation of the ELS for ELH detection in iMRI data.
The core component is a novel three-dimensional algorithm
based on Volumetric Local Thresholding (VOLT). The tool
was validated on artificial and prospective real-world data
sets.
Materials andmethods
Data sets
The study included three different data sets: data set 1 (D1,
development data set) was used to develop the novel ELH
detection algorithm based on Volumetric Local Threshold-
ing (VOLT). Data set 2 (D2, artificial validation data set) and
data set 3 (D3, prospective validation data set) were used to
validate VOLT on entirely unseen data.
D1 and D3 included real-world data sets from consecutive
patients from the interdisciplinary German Center for Ver-
tigo and Balance Disorders (DSGZ) of the Munich Univer-
sity Hospital (LMU) between 2015 and 2019. Institutional
Review Board approval was obtained before the initiation
of the study (no 64115). Included patients had presented
with episodic vertigo attacks [19] and undergone iMRI as
part of their indicated clinical diagnostic workup to evalu-
ate their ELS. Their data sets were included after they had
given oral and written consent following the Declaration of
Helsinki. The inclusion criteria were age above 18years.
Exclusion criteria were any MR-related contraindications
[20], poor image quality, or missing MR sequences. D1
included 210 inner ears of 105 consecutive patients (50
male; aged 19–84years, mean age 50.4±17.1years), and
D3 included 20 inner ears of 10 consecutive patients (5
male, aged 31–69years, mean age: 46.8±14.4years). D1
and D3 did not differ significantly concerning age, gender,
the grade of endolymphatic hydrops (ELH), or data quality
(intensity, mean grayscale value). A detailed description of
D1and D3 is given in Table1.
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S187Journal of Neurology (2020) 267 (Suppl 1):S185–S196
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As an artificial data set, D2 provided a known ground
truth to test and compare VOLT’s performance to an adapted
version of Otsu’s method [21], which is a recognized fore-
ground/background segmentation algorithm based on global
thresholding at an optimal histogram-derived cutoff. D2 con-
sisted 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). To this structural basis signal, two types of noise
were added, which imitate the real-world variability of MRI
signals [22]. The noise was added stepwise in the form of
increasing blurriness noise (Gaussian blur kernel, SD range
1–6 voxel in x/y/z-direction; SD = standard deviations) or
increasing scatter noise (Gaussian, SD range of intensity
variation: 0–50 SD). D2 and its varying levels of noise can
be viewed in Fig.1a.
D1 andD3–Clinical diagnosis andmeasurement
oftheauditory, semicircular canal, andotolith
functions
Patients were clinically diagnosed according to the interna-
tional guidelines, most of the classification committee of the
international Bárány Society (www.jvr-web.org/ICVD.html
or www.baran ysoci ety.nl) for the diagnosis of vestibular
migraine [23], Menière’s disease [24], vestibular paroxys-
mia [25], bilateral vestibulopathy [26], acute unilateral ves-
tibulopathy/vestibular neuritis [27] and benign paroxysmal
positional vertigo [28]. The diagnoses of the patients within
D1 and D3 can be viewed in Table1.
Diagnostic workup included a careful neurological and
neuro-otological examination including neuro-orthoptic
assessment (e.g., Frenzel goggles; fundus photography
and adjustments of the subjective visual vertical (SVV) for
graviceptive vestibular function, for methods, see [29]),
video-oculography during the head-impulse test (vHIT)
for dynamic vestibular function (for methods, see [30, 31]),
audiometry, and MR imaging of the whole brain including
the cerebellopontine angle and brainstem.
D1 andD3–Sequence protocol andgrading
ofthedelayed gadolinium‑enhanced ivMRI
oftheinner ear
Four hours after intravenous injection of a standard dose
(0.1ml/kg body weight, i.e., 0.1mmol/kg body weight) of
Gadobutrol (Gadovist®, Bayer, Leverkusen, Germany),
MR imaging data were acquired in a whole-body 3T MR
scanner (Magnetom Skyra, Siemens Healthcare, Erlangen,
Germany) with a 20-channel head coil. Head movements
were minimalized in all three axes using a head position-
ing system for MRI (Crania Adult 01, Pearl Technology
AG, Schlieren, Switzerland). A 3D-FLAIR sequence was
used to differentiate endolymph from perilymph and bone,
and a CISS sequence to delineate the total inner ear fluid
space from the surrounding bone. A T2-weighted, three-
dimensional, fluid-attenuated inversion recovery sequence
Table 1 Description of the real-
world data sets
D1 and D3 included data sets from consecutive patients from the interdisciplinary German Center for Ver-
tigo and Balance Disorders (DSGZ), Munich, Germany. Included patients had presented with episodic ver-
tigo attacks and undergone delayed intravenous gadolinium-enhanced magnetic resonance imaging of the
inner ear (iMRI) as part of their indicated clinical diagnostic workup. Patients were clinically diagnosed
according to the several international guidelines, most of the classification committee of the international
Bárány Society (https ://www.jvr-web.org/ICVD.html or https ://www.baran ysoci ety.nl) and included the
diagnosis of VM [23], MD [24], VP [25], BPPV [26], BVP [1] and acute unilateral vestibulopathy/vestibu-
lar neuritis [2]. Grading of the ELH in the vestibulum and cochlea was based on criteria described previ-
ously [3], which constitutes a fusion of two classification systems [4, 5]. D1 and D3 did not differ signifi-
cantly concerning age, gender, the grade of ELH, or data quality
± standard deviation, BPPV benign paroxysmal positional vertigo, BVP bilateral vestibulopathy, ELH
endolymphatic hydrops, ELS endolymphatic space, iMRI delayed intravenous gadolinium-enhanced mag-
netic resonance imaging of the inner ear, MD Menière’s disease, N number of participants, VM vestibular
migraine, VP vestibular paroxysmia
N (gender) Age Diagnosis ELH ELH grade Data Quality
D1 105
(50 male)
50.4±17.1
range 19–84
32% VM (n = 33)
28% MD (n= 29)
18% NV (n = 19)
17% VP (n = 18)
3% BVP (n = 4)
2% BPPV (n = 2)
97 out of 210 ears
46.2%
0.7±0.8
Range 0–3
1.1±0.3
Range 0.3–2.3
D3 10
(5 male)
46.8±14.4
range 31–69
10% VM (n = 1)
70% MD (n = 7)
10% NV (n = 1)
10% BPPV (n = 1)
7 out of 20 ears
35%
0.7±0.9
Range 0–2.5
1.1±0.3
Range 0.3–1.6
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S188 Journal of Neurology (2020) 267 (Suppl 1):S185–S196
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(3D-FLAIR) with the following parameters: TR 6000ms,
TE 134ms, TI 2240ms, FOV 160 × 160mm2, 36 slices,
base resolution 320, averages 1, acceleration factor of 2
using a parallel imaging technique with a generalized auto-
calibrating partially parallel acquisition (GRAPPA) algo-
rithm, slice thickness 0.5mm, acquisition time 15:08min
was carried out. A high-resolution, strongly T2-weighted,
3D constructive interference steady state (CISS) sequence
of the temporal bones was performed to evaluate the anat-
omy of the whole-fluid-filled labyrinthine spaces with the
following parameters: TR 1000ms, TE 133ms, FA 100°,
FOV 192 × 192mm2, 56 slices, base resolution 384, aver-
ages 4, acceleration factor of 2 using GRAPPA algorithm,
slice thickness of 0.5mm and acquisition time 8:36min. The
presence of ELH was observed on the 3D-FLAIR images as
enlarged negative-signal spaces inside the labyrinth, accord-
ing to a previously reported method [32, 33]. The decision to
apply a single-dose contrast agent was made because of the
ongoing discussion about gadolinium deposition within the
dentate nucleus and globus pallidus after repeated adminis-
tration of gadolinium-based contrast agents [3437]. It was
not considered ethical to apply higher doses of contrast agent
if not necessary. Accordingly, only patients with a diagnostic
benefit were included in the study.
Evaluation of the iMRI and grading of the ELS was per-
formed independently by two experienced head and neck
radiologists and a neurologist who was blinded to the clini-
cal patient data. If discrepancies arose, a consensus was
reached by discussion. The characterization of the ELS in
the vestibulum and cochlea was based on criteria previously
described [12], which constitutes a fusion of two classifica-
tion systems [38, 39]. D1 and D3 did not differ significantly
concerning the grade of ELH. An overview of ELH grade
and data quality for data sets D1 and D3 can be viewed in
Table1.
D1–Development oftheautomatic segmentation
tool forELH detection based onVolumetric Local
Thresholding (VOLT)
VOLT was developed on the real-world data set D1 using
exclusively universal access software, namely 3D Slicer
version 4.11 toolbox [40] including the TOMAAT plugin
[15], as well as ImageJ Fiji [41] including the “Fuzzy and
artificial neural networks image processing toolbox” [42]
and the “MorphoLibJ Toolbox” [43](see an overview of the
overall pipeline including VOLT-based ELS segmentation
in Fig.2a, b).
Data pre‑processing included thefollowingsteps
VOLT operates on a pre-segmented region-of-interest (ROI)
of the inner ear, which requires a series of data pre-process-
ing steps. First, FLAIR and CISS sequences were interpo-
lated to a voxel size of 0.25mm × 0.25mm × 0.25mm using
a bicubic interpolation algorithm in ImageJ. Then, left and
Fig. 1 D2 artificial data set–visualization and results. As an arti-
ficial data set, D2 provided a known ground truth to test and com-
pare VOLT cutoff versions to Otsu’s method. a A transversal slice-
wise visualization of D2 in the middle. D2 can be viewed in the very
middle and included an 8-bit cuboid volume with different sizes of
cylindrical and cuboid-shaped cutouts (signal). To this signal differ-
ent types of real-world MRI imitating noise were added stepwise in
the form of increasing blurriness (Gaussian blur kernel, SD range
1–6 voxel in x/y/z-direction; SD = standard deviations, visualized to
the left) and increasing scatter (SD range of intensity variation: 0–50
SD, visualized to the right). b Based on empirical observations in the
development data set (D1), VOLT was compared to Otsu’s method
(O = grey) at three cutoff variations (c6 = forest green, c8 = red,
c10 = yellow). Both VOLT cutoff versions and Otsu’s method fared
better with blurriness noise (x-axis of the left graph) in compari-
son with scatter noise (x-axis of the right graph). More specifically,
VOLT cutoff versions showed a high level of agreement in terms of
Dice overlap (y-axis within the graphs) with Otsu’s scores in data sets
with low noise levels (please compare blurriness 2, framed in mint
green and scatter 20, framed in pink). The higher the noise level, the
more VOLT cutoff versions outperformed Otsu’s method (please note
blurriness 5, framed in purple and scatter 50, framed in blue). The
corresponding output (c) can easily be compared with the ground-
truth by following said color frames. D2 data set 2, c6 cutoff 6, c8
cutoff 8, c10 cutoff 10, O Otsu’s method
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S189Journal of Neurology (2020) 267 (Suppl 1):S185–S196
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Fig. 2 VOLT flowchart and output examples. The flowchart shows a
step-by-step overview of the VOLT processing pipeline of a left inner
ear. The different steps correspond to the boxes in a counterclockwise
fashion (a, b, c). a Describes data pre-processing, b data processing,
and c shows output examples. Within each box, processing steps fol-
lowing orange arrows indicate the order of the main program steps,
and green arrows indicate supporting steps. Data pre-processing (a)
consists of cropping the inner ear from CISS and FLAIR MR images
(only step requiring user input), co-registration, and using a cloud-
based deep convolutional neural network (CNN) to create a mask
of the inner ear. During data processing, (b) the mask is dilated to
include a small seam around the inner ear region-of-interest (ROI).
Then, a fusion volume is created, contrast-enhanced, and the fusion
volume is 3D reconstructed. VOLT is performed, volumes are
reconstructed into a transversal plane and re-sampled into one vol-
ume. After 3D blurring, single-voxel noise is removed, and a three-
dimensional outline based on the mask is added to the final result.
(c) depicts two output examples of the right inner ear. The upper row
shows the corresponding cropped FLAIR-MR image; the middle row
shows a 2D depiction of the VOLT output, and the lower row shows
the 3D visualization of VOLT-output. The inner ear to the left dis-
plays no endolymphatic hydrops (ELH). The inner ear to the right
displays an ELH grade 2. CISS constructive interference in steady-
state, MR magnetic resonance, FLAIR fluid-attenuated inversion
recovery, VOLT volumetric local thresholding
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S190 Journal of Neurology (2020) 267 (Suppl 1):S185–S196
1 3
right inner ears were cropped using a rectangular selection,
converted to nrrd-files, and imported into 3D-Slicer. To
obtain an ROI of the overall inner ear region, a voxel-wise
segmentation of this cropped region needed to be obtained.
To this end, we applied a recently proposed deep convolu-
tional neural network (CNN), deployed via the TOMAAT
module in 3D–Slicer [44]. This step first normalizes the
orientation of the cropped volume by affine image registra-
tion (BRAINSFit toolkit [45]) and then applies a pre-trained
volumetric CNN with V-net architecture [46]. The V-net
output yields a segmentation into two labels, either inner
ear or background. The “inner-ear” segmentation, hereafter
referred to as “mask”, was converted into an 8-bit binary
volume and volumetrically dilated using 3D morphological
filtering. As dilation adds a thin shell of anatomy surround-
ing the inner ear, this step allows the amount of false-nega-
tive classifications by the VOLT segmentation algorithm to
be reduced. An overview of the pre-processing required for
VOLT-based ELS segmentation can be viewed in Fig.2a.
Data processing included thefollowingsteps
Two locally adaptive thresholding algorithms (“Bernsen”
[47] and “Mean”) were used in the three planes, and in four
varying radii, respectively, to differentiate between endo-
lymphatic fluid (EF) and non–endolymphatic fluid (NEF).
These intermediate segmentations were then reconstructed
in a transversal plane and aggregated into one final segmen-
tation volume. Close attention was paid to avoid the inclu-
sion of false positives into the ELS, by considering only
voxels within a volumetrically strict outer shell of the inner
ear (see pre-processing). As a next step, single-voxel-noise
was reduced using a 3D Gaussian blurring algorithm. The
first mask was used to create a single pixel-sized borderline
(= 0.25mm) in all three planes to ultimately avoid false-
positive classifications in the corner regions of these three
planes. The resulting 3D volume can then be regarded as a
probabilistic map of the inner ear, which included the clas-
sification into its two different compartments (endolym-
phatic and perilymphatic space). The final classification then
strongly depends on the chosen cutoff. Each cutoff matches a
percentage of positive classifications. For example, cutoff 6
(c6) corresponds to 79.2%, cutoff 8 (c8) to 70.8% and cutoff
10 (c10) to 62.5% classifications into endolymphatic space.
Based on empirical observations in the development data
set (D1), VOLT was validated at three cutoff variations (c6,
c8, c10; Fig.2b).
Automatization andpipeline creation
A script written in the IJ1M-macro-language was used
to automate pre-processing and processing in FIJI. User
input was required solely for supervision purposes during
cropping, registration, and segmentation. The remaining
features (pre-processing, volumetric reconstruction, contrast
enhancement, fusion, thresholding, and post-processing)
work automatically.
D1, D2 andD3–Methods forvalidation
VOLT with three different cutoffs (c6, c8, c10) was validated
on the artificial data set D2 and the prospective real-world
data set D3. Segmentation accuracy was evaluated using
the Sørensen-Dice overlap coefficient, which is defined as
2 * |X Y|/|X| +|Y| for segmentations X and Y [48], as a
measure of region overlap between gold standard segmenta-
tion and the automatically obtained segmentations from the
VOLT pipeline.
Segmentation precision was estimated by comparing
the volume of the ELS (VE) between segmentation meth-
ods. Structurally, the human inner ear can be pictured as
an external, bony hose system (called the bony labyrinth,
containing perilymph) and an inner hose system (called the
membranous labyrinth, containing endolymph). The total
lymph fluid space includes the inner hose system’s ELS and
the surrounding perilymphatic space.
Receiver operating characteristics (ROC) analysis was
used to show the (in)dependence of the performance of the
methods from the grade of the ELH or the distribution of
the fluids within the total fluid space (TFL) and the SNR of
the iMRI data set.
D1, D2 andD3 statistics andmap display
The data were analyzed with SPSS 20.0 (SPSS, Chicago, IL,
USA). Differences between data sets overall were assessed
using a paired t-test, which was Bonferroni-corrected for
multiple testing and viewed at p < 0.01 and p < 0.05. Lin-
ear agreement between parameter pairs was calculated for
each method separately using the two-sided Spearman’s cor-
relation coefficient and reported at a significance level of
p < 0.01 and p < 0.05. For Receiver operating characteristics
(ROC) analysis, the original Fortran program JLABROC4
(by Charles Metz and colleagues, Department of Radiology,
University of Chicago; Java translation by John Eng, Russel
H Morgan Department of Radiology and Radiological Sci-
ence, Johns Hopkins University, Baltimore, Maryland, USA,
Version 2.0, March 2017) was used.
Results
VOLT implementation onD1
After implementation on data set D1, the novel tool for auto-
matic segmentation of the endolymphatic space (ELS) with
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S191Journal of Neurology (2020) 267 (Suppl 1):S185–S196
1 3
a novel algorithm based on Volumetric Local Thresholding
(VOLT) ran smoothly and showed no operational or sta-
bility issues. VOLT does not require especially powerful
hardware or closed-source software. The only prerequisite
is the installation of universal access software, namely 3D
Slicer toolbox [40] including the TOMAAT plugin [15], as
well as ImageJ Fiji [41] including the “Fuzzy and artificial
neural networks image processing toolbox” [42] and the
“MorphoLibJ Toolbox” [43].
The only step requiring user input was the cropping step.
While this step required a rough selection of the inner ear
and could easily be automatized, it allowed a quick and easy
visual assessment of the source images and was therefore
considered a suitable quality control mechanism. Cropping
was performed in order to reduce computation time as well
as allow for easier registration of the inner ears; the registra-
tion step was necessary to ensure correct positioning of the
CISS-based hull relative to the FLAIR. For both registration
and CNN segmentation, the necessary user input was limited
to entering parameters and starting the process. After the
CNN segmentation the user had to save the segmentations
as a new file manually. As an orientation, pre-processing
steps of one single-subject data set can be performed in less
than ten minutes by an experienced user on a standard con-
sumer laptop (Windows10 (64Bit), Intel® Core i5-4200U
@1,6GHz, 8GB RAM).
Data processing steps are fully automated and run with-
out further user input in less than 60s. Volumetric local
thresholds can be adapted to signal-to-noise ratio (SNR) of
different data sets. Output files include 3D volumetric quan-
tification of TLS and ELS in mm3 and a 3D visualization of
the inner ear. Examples of single-subject VOLT-based inner
ear segmentations show different grades of ELH (Fig.2c).
VOLT performance onarticial data set D2
D2 was created to have a ground truth data set featuring
challenges found in inner ear imaging, namely low contrast
and high noise. Similar to actual iMRI, the regions of inter-
est were three-dimensional volumes of different sizes. This
proofed to be difficult for 2D-algorithms, whereas three-
dimensional methods could analyze the data set better.
As an artificial data set, D2 provided a known ground
truth to test and compare VOLT’s performance to an adapted
version of Otsu’s method (O) [21], which is a recognized
foreground/background segmentation algorithm based on
global thresholding at an optimal histogram-derived cutoff.
Based on empirical observations in the development data
set (D1), VOLT was compared to O at three cutoff varia-
tions (c6, c8, c10). On average, over all noise conditions,
the Dice score (DS) of VOLT cutoff versions (c6: 90%, c8:
92%; c10: 92%) outperformed Otsu’s method (82%). Both
VOLT cutoff versions and Otsu’s method fared better with
blurriness noise (DS:O: 91%; c6: 92%, c8: 93%; c10: 94%)
in comparison with scatter noise (DS O: 82%; c6: 87%, c8:
91%; c10: 90%). More specifically, VOLT cutoff versions
showed a high level of agreement in terms of Dice overlap
with Otsu’s scores in data sets with low noise levels (Bl
1–4; Sc 10). The higher the noise level, however, the more
VOLT cutoff versions outperformed Otsu’s method (Bl 5–6;
Sc 20–60), with c8 showing an overall best performance
independent of noise levels. All results are presented in
Table2 and Fig.1b, c.
VOLT performance onprospective real‑world data
set D3
D3 included previously entirely unseen real-world data
sets from 10 consecutive patients (= 20 inner ears) and was
used to validate VOLT on entirely unseen data. Ear-specific
segmentation accuracy was evaluated using the Sørensen-
Dice overlap coefficient (DS), and segmentation precision
were estimated by comparing the volume of the ELS (VE).
Performance (DS) of VOLT with the three different cutoffs
c6: 97.0%±0.7, c8: 96.6%±0.8, c10: 95.9% 97%±0.9)
highly overlapped with the manual segmentation. On aver-
age, c8 gave a close representation of the actual volume
seen in the manual segmentation, while c6 tended to under-
estimate and c10 to overestimate the endolymphatic space
volume methodically. Note that the grade of ELH corre-
lated significantly with the endolymphatic volume of both
the manual segmentation method (two-sided, r(18) = 0.475,
p = 0.034) and with VOLT cutoff variations c6 (two-sided,
r(18) = 0.553, p = 0.011)–c8 (two-sided, r(18) = 0.566,
p = 0.009)–c10 (two-sided, r(18) = 0.569, p = 0.009).
Receiver operating characteristics (ROC) analysis showed
the grade of the ELH to be a good classifier for the computed
volume of the ELS (fitted ROC area: 0.9). Table2 shows an
overview of the performance and accuracy results of each
segmentation method. Figure3 gives an ear-specific over-
view of each validation parameter.
Discussion
An open-source tool for automatic volumetric segmenta-
tion of the endolymphatic space (ELS) for endolymphatic
hydrops (ELH) detection in intravenous, delayed, gadolin-
ium-enhanced magnetic resonance imaging of the inner ear
(iMRI) data was developed on a real-world data set includ-
ing 210 inner ears. The core component is a novel algorithm
based on Volumetric Local Thresholding (VOLT). Tool vali-
dation in two data sets, one artificial data set that provided
a known ground truth and one real-world that included 20
previously unseen inner ears, resulted in a high level of per-
formance and accuracy in comparison with the respective
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S192 Journal of Neurology (2020) 267 (Suppl 1):S185–S196
1 3
gold standard (Otsu’s method and manual segmentation).
In the case of the artificial data set, VOLT outperformed the
gold standard in higher noise levels. VOLT endolymph vol-
ume significantly correlated with the clinical grading of the
ELS. VOLT operates on a pre-segmented region-of-interest
(ROI) of the inner ear, which requires a series of data pre-
processing steps (duration < 10min). Data processing steps
are fully automated and run without further user input in
less than 60s.
VOLT–performance andusability
Objective and volumetric quantification is a necessary step
to assess and compare ELH results between studies and
hospitals. So far, the clinical gold standard assessment
of the ELS is based on a semi-quantitative and subjec-
tive grading reliant on a few MR slices in the transversal
plane. In addition, different ELH classifications are being
used in parallel [38, 39, 49, 50]. While manual volumetric
segmentation is the gold standard for volumetric quan-
tification, it is highly subjective and dependent on the
rater’s experience and knowledge, not to mention time-
consuming. VOLT allows objective, easily reproducible,
and reliable stand-alone volumetric ELH quantification
and grading, which closely matches manual segmentation,
highly correlates with clinical ELH grading, and performs
particularly well in data with a low signal-to-noise ratio.
The main advantage of VOLT is its local thresholding
algorithm, which enables more flexible and stable results
in comparison to global thresholding algorithms (such as
Otsu’s method). Inhomogeneous image intensities and
local brightness variations are adequately compensated
for [48, 51]. The robustness and flexibility of VOLT to
image artifacts can be further increased using different
radius sizes. Importantly, results in Fig.1 demonstrate that
VOLT does not yield perfect segmentation in the absence
of noise (not probable in real-world data), but instead
Table 2 Overview of results
As an artificial data set, D2 provided a known ground truth to test and compare VOLT cutoff versions to Otsu’s method (O). A shows an over-
view of the Dice scores (DS) of each segmentation method (Otsu’s, cutoff 6, cutoff 8, cutoff 10) concerning the real-world MRI imitating noise
that was added stepwise in the form of increasing blurriness noise (Bl, Gaussian blur kernel, SD range 1–6 voxel in x/y/z-direction; SD = stand-
ard deviations) or increasing scatter noise (Sc, SD range of intensity variation: 0–50 SD). For visualization of the added noise and results, see
Fig.1a. D3 included real-world data sets from consecutive patients from the interdisciplinary German Center for Vertigo and Balance Disorders,
Munich, Germany. Part B shows an overview of the results’ mean of each segmentation method (manual segmentation that was considered as
the gold standard and VOLT with three different cutoffs 6, 8, 10). Segmentation accuracy was evaluated using the Sørensen-Dice overlap coeffi-
cient, and segmentation precision were estimated by comparing the volume of the ELS (VE). The ratio VE/M was supplied to show the deviation
of each cutoff from the gold standard, which was the manual segmentation. The VE ranges include all different grades of endolymphatic hydrops
± standard deviation, Bl blurriness, DS Dice score, Sc scatter, VE volume of the endolymphatic space, VT volume of the total fluid space
A
Data set Noise Scale Otsu’s Cutoff 6 Cutoff 8 Cutoff 10
D2 BI 1 99.4% 95.0% 98.0% 98.7%
2 97.6% 93.7% 95.5% 96.7%
3 93.8% 92.0% 93.4% 94.6%
4 90.3% 90.3% 91.2% 92.7%
6 85.8% 90.0% 90.8% 91.5%
Sc 10 98.5% 88.4% 93.6% 97.9%
20 88.7% 87.9% 93.0% 96.9%
30 81.2% 87.7% 92.2% 93.5%
40 76.7% 87.4% 90.5% 87.6%
50 74.3% 86.5% 88.0% 82.3%
60 73.2% 85.6% 86.3% 80.5%
B
Data set Validation M Cutoff 6 Cutoff 8 Cutoff 10
D3 DS Gold standard 97.0%±0.7
range 95.6–97.9
96.6%±0.8
range 95.0–97.7
95.9%±0.9
range 93.8–97.2
VE16.7mm3±5.5
range 8.8–30.7
11.5 mm3±5.7
range 5.0–25.5
17.1 mm3±7.4
range 8.4–33.6
23.3 mm3±8.7
range: 13.0–41.0
VE/M 1 0.7±0.2
range 0.4–0.9
1±0.2
range 0.7–1.5
1.4±0.3
range 1.0–2.1
VT276.2mm3±37.6 (range 223.6–347.6)
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S193Journal of Neurology (2020) 267 (Suppl 1):S185–S196
1 3
performs more favorably and more stably in the presence
of increased noise (very probable in real-world data).
Current ELS MR volumetric assessment approaches
remain few and involve a manual or semi-automatic segmen-
tation [6, 18, 50, 52]. Already a considerable improvement,
these approaches require lengthy user interaction that is not
suitable for use in more extensive group studies or clini-
cal routine. Also, the software used tends to be attached to
expensive software, and the uncompiled source is not avail-
able for public review.
VOLT runs smoothly and does not require especially
powerful hardware or closed-source software. As an orien-
tation, pre-processing steps of one data set can be performed
in less than ten minutes by an experienced user on a standard
consumer laptop. Data processing steps are fully automated
and run without further user input in less than 60s. Volu-
metric local thresholds can be adapted to the signal-to-noise
ratio (SNR) of different data sets. Output files include 3D
volumetric quantification of TLS and ELS in mm3 and a 3D
visualization of the inner ear. The endolymphatic volumes
conformed to those previously reported [53, 54].
VOLT exibilitydeep learning isbenecial
butnotarequirement
Inner ear segmentation is a prerequisite step for VOLT-
based ELH segmentation and is currently performed via
a novel CNN-based deep learning approach [43], which
is deployed as a module in 3D-Slicer [40]. This CNN was
trained in-house at our department, on a separate iMRI data
set obtained on the same MRI scanner and with the same
imaging sequence parameters as our study. As such, this
method was a natural choice for inner ear ROI segmentation
in our data set, especially because segmentations were not
only highly accurate but also obtainable in comparably fast
execution time (< 5s). A downside is that this network likely
has difficulties in generalizing to data from other scanners or
imaging sequence settings, e.g., from other clinics. There-
fore, we do not assume a TOMAAT/V-Net segmentation as a
fixed component of the current ELH segmentation pipeline.
The inner ear ROI can also be obtained by other segmenta-
tion approaches, most prominently using atlas-based reg-
istration. Recently, two in-vivo MRI atlases and templates
were proposed, one offering a probabilistic segmentation
of the inner ear’s bony labyrinth [46], the other offering a
high-resolution multivariate template for T1-, T2- and CISS-
weighted MRI imaging [41]. Both atlases can yield accurate
segmentation of the inner ear ROI while being much more
generalizable to MRI data from previously unseen scan-
ners or acquisition sites, in particular, if multivariate MRI
appearances are available as in [41]. The downside of atlas-
based segmentation is the high computational complexity of
deformable atlas registration algorithms that align the atlas
Fig. 3 D3 prospective validation data set results. D3 was used to vali-
date VOLT on entirely unseen real-world data (20 inner ears). VOLT
with the three variations cutoff 6 (c6 = dark green), cutoff 8 (c8 = red),
and cutoff 10 (c10 = yellow) were compared to manual (M) segmenta-
tion (= grey, that was considered the gold standard). Ear-specific seg-
mentation accuracy was evaluated using the Sørensen-Dice overlap
coefficient (DS, upper graph), and segmentation precision were esti-
mated by comparing the volume of the ELS (V, middle graph). Over-
all, DS of all three VOLT variations was high (c6: 97.0%
±
0.7, c8:
96.6%
±
0.8, c10: 95.9% 97%
±
0.9). The influence of endolymphatic
hydrops (ELH = colored light green) and data quality (dQ = colored
blue) can easily be seen in the lowest graph. Data quality was defined
as mean the greyscale value (or intensity). Note that the grade of ELH
correlated significantly with the endolymphatic volume of both the
manual segmentation method (p < 0.05) and VOLT cutoff variations
c6-8–10 (p < 0.01). c6 cutoff 6, c8 cutoff 8, c10 cutoff 10, D3 data set
3, dQ data quality, DS Dice score, M manual segmentation
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S194 Journal of Neurology (2020) 267 (Suppl 1):S185–S196
1 3
to the target volume. With carefully tuned parametrizations,
such algorithms can achieve highly accurate segmentation,
but segmentations can take 10min to 2h per volume [47],
compared to < 5s computation time for deep neural nets
such as [43]. Overall, we, therefore, recommend the usage
of deep neural nets for inner ear segmentation predictions;
however, the correct way to generalize the network to new
sites, e.g., via transfer learning, remains to be established
in future work.
Methodical limitations
There are methodical limitations in the current study that
need to be considered in the interpretation of the data. First,
the performance of VOLT is highly dependent on the seg-
mentation of the inner ear. An inner ear mask that includes
parts of the dark background voxels surrounding the inner
ear structures would lead to a false-positive attribution to
the ELS. VOLT’s high performance and accuracy values
are probably in part attributable to the novel CNN-based
deep learning approach. Second, VOLT does not include
any anatomical knowledge. In the best case, this means that
the algorithm is entirely unbiased, i.e., not influenced by any
prior morphological assumptions. The downside is a lack
of exclusion of apparent errors that would be noticed by the
human examiner.
An example would be segmentation errors that included
surrounding structures into the ROI. A human examiner
would know not to expect endolymph in the outermost tips
of the cochlea or vestibulum. However, an algorithm does
not. This is one reason VOLT is designed unusually strict
in margin areas. Finally, VOLT (or any ELS segmentation
method) is by nature highly dependent upon the resolution
and contrast of the MRI raw data to be able to distinguish
between endolymphatic and perilymphatic space.
Conclusion
We propose a novel pipeline for the automatic segmenta-
tion of endolymphatic hydrops in inner ear MRI. The core
component is a novel algorithm based on Volumetric Local
Thresholding (VOLT). Tool validation on artificial and real-
world data resulted in a high level of performance and accu-
racy, in particular in low signal-to-noise ratio. ELS volume
significantly correlated (p < 0.01) with the clinical grading
of the ELS. A generic version of our three-dimensional
thresholding algorithm has been made available to the sci-
entific community via GitHub as an ImageJ-Plugin (https ://
githu b.com/j-gerb/3d-thres holdi ng/tree/maste r).
Acknowledgements Open Access funding provided by Projekt DEAL.
Partially funded by the Friedrich-Baur-Stiftung (FBS), the Gradu-
ate School of Systemic Neurosciences (GSN), the German Federal
Ministry of Education and Research (BMBF) in connection with the
foundation of the German Center for Vertigo and Balance Disorders
(DSGZ), grant number 01 EO 0901. This is part of the dissertation of
Johannes Gerb. We thank K. Göttlinger for copyediting the manuscript.
Compliance with ethical standards
Conflicts of interest The authors declare they have no competing fi-
nancial interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
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... The second difficulty is distinguishing the two different fluid spaces within the TFS (22,23), namely ELS within the membranous labyrinth and the surrounding perilymphatic space (PLS) within the bony labyrinth. Current semi-automatic (24)(25)(26) or automatic (27,28) 3D ELS quantification methods have mostly concentrated on ELS differentiation within TFS. ...
... This work was conducted at the interdisciplinary German Center for Vertigo and Balance Disorders (DSGZ) and the Neurology Department of the Munich University Hospital (LMU) between 2015 and 2019. This study used previously published datasets (10,27,30,34,35). Institutional Review Board approval was obtained before the initiation of the study (no. ...
... IE-Vnet bridges the current gap existing for available automatic 3D ELS quantification methods. In particular, its input can be seamlessly combined with a previously published opensource pipeline for automatic iMRI ELS segmentation (27) via the TOMAAT module (81) in 3DSlicer (82). ...
Article
Full-text available
Background In-vivo MR-based high-resolution volumetric quantification methods of the endolymphatic hydrops (ELH) are highly dependent on a reliable segmentation of the inner ear's total fluid space (TFS). This study aimed to develop a novel open-source inner ear TFS segmentation approach using a dedicated deep learning (DL) model.Methods The model was based on a V-Net architecture (IE-Vnet) and a multivariate (MR scans: T1, T2, FLAIR, SPACE) training dataset (D1, 179 consecutive patients with peripheral vestibulocochlear syndromes). Ground-truth TFS masks were generated in a semi-manual, atlas-assisted approach. IE-Vnet model segmentation performance, generalizability, and robustness to domain shift were evaluated on four heterogenous test datasets (D2-D5, n = 4 × 20 ears).ResultsThe IE-Vnet model predicted TFS masks with consistently high congruence to the ground-truth in all test datasets (Dice overlap coefficient: 0.9 ± 0.02, Hausdorff maximum surface distance: 0.93 ± 0.71 mm, mean surface distance: 0.022 ± 0.005 mm) without significant difference concerning side (two-sided Wilcoxon signed-rank test, p>0.05), or dataset (Kruskal-Wallis test, p>0.05; post-hoc Mann-Whitney U, FDR-corrected, all p>0.2). Prediction took 0.2 s, and was 2,000 times faster than a state-of-the-art atlas-based segmentation method.ConclusionIE-Vnet TFS segmentation demonstrated high accuracy, robustness toward domain shift, and rapid prediction times. Its output works seamlessly with a previously published open-source pipeline for automatic ELS segmentation. IE-Vnet could serve as a core tool for high-volume trans-institutional studies of the inner ear. Code and pre-trained models are available free and open-source under https://github.com/pydsgz/IEVNet.
... Data acquisition protocols have undergone a continuous optimization of MR sequences (14,15), as well as a steady minimization of procedural invasiveness (via a shift from intratympanic to intravenous application), duration and Gd dosage (16)(17)(18). A variety of cochlear and vestibular ELH quantification conventions have been suggested, including ELS semi-quantitative visual grading (19)(20)(21)(22)(23)(24)(25), manual measurement (26)(27)(28), semi-automatic (29,30), and automatic algorithmic area ratio (AR), and volumetric segmentation (31,32). ...
... Segmentation of the total fluid space (TFS) was based on a recently proposed (Ahmadi et al., under review) and pretrained volumetric deep convolutional neural network (CNN) with V-net architecture (51) that was deployed via the TOMAAT module (52) in 3D-Slicer toolbox [version 4.11 (53)]. ELS and PLS were differentiated within the TFS using Volumetric Local Thresholding [VOLT; (31)] using ImageJ Fiji (48) with the "Fuzzy and artificial neural networks image processing toolbox" (54) and the "MorphoLibJ Toolbox" (47). ...
... Yet here, too, methodological variations affect reproducibility and availability of results. The critical points are the segmentation of the inner ear from the background [manually (29), via atlas (76,77), or CNN (31); (Ahmadi et al., under review)] and the ELS and PLS from the TFS [manually (26), semi-automatic (29), automatic (31)], as well as the availability of the software solutions [commercial (26,28,29,78) vs. open source (31)]. The less human-dependent and the more automated, the more reproducible the method in most cases. ...
Article
Full-text available
Introduction: Verification of endolymphatic hydrops (ELH) via intravenous delayed Gadolinium (Gd) enhanced magnetic resonance imaging of the inner ear (iMRI) is developing into a standard clinical tool to investigate vestibulo-cochlear syndromes [1,2]. Methods: 108 participants, 75 patients with Meniere’s disease (MD; 55.2±14.9 years) and 33 vestibular healthy controls (HC; 46.4±15.6 years) were included to examine how (i) MR acquisition protocols influence the signal within endolymphatic space (ELS), (ii) ELS quantification methods correlate to each other [3,4] and clinical data, and finally, (iii) ELS extent influences MR-signals. Results: Semi-quantitative (SQ) and 2D- or 3D-quantifications of the ELS were independent of signal intensity (SI) and signal-to-noise ratio (SNR) within 0.1 to 0.2 mmol/kg Gd dosage and 4h±30 min time delay (FWE corrected, p<0.05, Figure 1). Used methods correlated strongly (0.3-0.8) and were highly reproducible across raters, thresholds. 3D-quantifications showed least variability. Asymmetry indices and normalized ELH were most useful for predicting quantitative clinical data. ELH size influenced SI, but not SNR. SI could not predict the presence of ELH. Conclusion: 1) Gd dosage of 0.1-0.2 mmol/kg after 4h30 min time delay suffices for ELS quantification. 2) A clinical SQ grading classification including a standardized level of evaluation reconstructed to anatomical fixpoints is needed. 3) ELS 3D-quantification methods are best suited for correlations with clinical variables, should include both ears and ELS values reported relative or normalized to size. 4) ELH leads to mild SI increases. However, these signal changes cannot be used to predict the presence of ELH. References: [1] Strupp M, Brandt T, Dieterich M. Vertigo - Leitsymptom Schwindel. 3rd ed. Berlin Heidelberg: Springer-Verlag (2021). doi: 10.1007/978-3-662-61397-9. [2] Pyykkö I, Zou J, Gürkov R, Naganawa S, Nakashima T. “Imaging of Temporal Bone,” in Advances in Oto-Rhino-Laryngology, eds. J. Lea, D. Pothier (S. Karger AG), 12–31. doi:10.1159/000490268. [2] Nakashima T, Naganawa S, Pyykko I, Gibson WPR, Sone M, Nakata S, Teranishi M. Grading of endolymphatic hydrops using magnetic resonance imaging. Acta Otolaryngol Suppl (2009)5–8. doi:10.1080/00016480902729827. [3] Gerb J, Ahmadi SA, Kierig E, Ertl-Wagner B, Dieterich M, Kirsch V. VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI. J Neurol (2020) 267:185–196. doi:10.1007/s00415-020-10062-8. Legend • Download : Download high-res image (438KB) • Download : Download full-size image
... Data acquisition protocols have undergone a continuous optimization of MR sequences (14,15), as well as a steady minimization of procedural invasiveness (via a shift from intratympanic to intravenous application), duration and Gd dosage (16)(17)(18). A variety of cochlear and vestibular ELH quantification conventions have been suggested, including ELS semi-quantitative visual grading (19)(20)(21)(22)(23)(24)(25), manual measurement (26)(27)(28), semi-automatic (29,30), and automatic algorithmic area ratio (AR), and volumetric segmentation (31,32). ...
... Segmentation of the total fluid space (TFS) was based on a recently proposed (Ahmadi et al., under review) and pretrained volumetric deep convolutional neural network (CNN) with V-net architecture (51) that was deployed via the TOMAAT module (52) in 3D-Slicer toolbox [version 4.11 (53)]. ELS and PLS were differentiated within the TFS using Volumetric Local Thresholding [VOLT; (31)] using ImageJ Fiji (48) with the "Fuzzy and artificial neural networks image processing toolbox" (54) and the "MorphoLibJ Toolbox" (47). ...
... Yet here, too, methodological variations affect reproducibility and availability of results. The critical points are the segmentation of the inner ear from the background [manually (29), via atlas (76,77), or CNN (31); (Ahmadi et al., under review)] and the ELS and PLS from the TFS [manually (26), semi-automatic (29), automatic (31)], as well as the availability of the software solutions [commercial (26,28,29,78) vs. open source (31)]. The less human-dependent and the more automated, the more reproducible the method in most cases. ...
Article
Full-text available
In-vivo non-invasive verification of endolymphatic hydrops (ELH) by means of intravenous delayed gadolinium (Gd) enhanced magnetic resonance imaging of the inner ear (iMRI) is rapidly developing into a standard clinical tool to investigate peripheral vestibulo-cochlear syndromes. In this context, methodological comparative studies providing standardization and comparability between labs seem even more important, but so far very few are available. One hundred eight participants [75 patients with Meniere's disease (MD; 55.2 ± 14.9 years) and 33 vestibular healthy controls (HC; 46.4 ± 15.6 years)] were examined. The aim was to understand (i) how variations in acquisition protocols influence endolymphatic space (ELS) MR-signals; (ii) how ELS quantification methods correlate to each other or clinical data; and finally, (iii) how ELS extent influences MR-signals. Diagnostics included neuro-otological assessment, video-oculography during caloric stimulation, head-impulse test, audiometry, and iMRI. Data analysis provided semi-quantitative (SQ) visual grading and automatic algorithmic quantitative segmentation of ELS area [2D, mm2] and volume [3D, mm3] using deep learning-based segmentation and volumetric local thresholding. Within the range of 0.1–0.2 mmol/kg Gd dosage and a 4 h ± 30 min time delay, SQ grading and 2D- or 3D-quantifications were independent of signal intensity (SI) and signal-to-noise ratio (SNR; FWE corrected, p < 0.05). The ELS quantification methods used were highly reproducible across raters or thresholds and correlated strongly (0.3–0.8). However, 3D-quantifications showed the least variability. Asymmetry indices and normalized ELH proved the most useful for predicting quantitative clinical data. ELH size influenced SI (cochlear basal turn p < 0.001), but not SNR. SI could not predict the presence of ELH. In conclusion, (1) Gd dosage of 0.1–0.2 mmol/kg after 4 h ± 30 min time delay suffices for ELS quantification. (2) A consensus is needed on a clinical SQ grading classification including a standardized level of evaluation reconstructed to anatomical fixpoints. (3) 3D-quantification methods of the ELS are best suited for correlations with clinical variables and should include both ears and ELS values reported relative or normalized to size. (4) The presence of ELH increases signal intensity in the basal cochlear turn weakly, but cannot predict the presence of ELH.
... 3D-quantification of the ELS consisted of three steps: first, segmentation of the total fluid space (TFS) was based on IE-Vnet [50], a recently proposed and pre-trained volumetric deep learning algorithm with V-net architecture that was deployed via the TOMAAT module [51] in a 3D-Slicer toolbox (version 4.11 [36]). Second, ELS and perilymphatic space (PS) were differentiated within the TFS using Volumetric Local Thresholding (VOLT; [52]) with ImageJ Fiji [53], the "Fuzzy and artificial neural networks image processing toolbox" [54], and the "MorphoLibJ Toolbox" [55]. The resulting 3D volume included classification into two different compartments (ELS and PS), examined at cutoff 6. ...
... Third, measurements were performed using the 'Analyze Regions (3D)' plugin of the "MorpholibJ Toolbox" [55]. The method is described in more detail in previous publications [52]. ...
Article
Full-text available
Combining magnetic resonance imaging (MRI) sequences that permit the determination of vestibular nerve angulation (NA = change of nerve caliber or direction), structural nerve integrity via diffusion tensor imaging (DTI), and exclusion of endolymphatic hydrops (ELH) via delayed gadolinium-enhanced MRI of the inner ear (iMRI) could increase the diagnostic accuracy in patients with vestibular paroxysmia (VP). Thirty-six participants were examined, 18 with VP (52.6 ± 18.1 years) and 18 age-matched with normal vestibulocochlear testing (NP 50.3 ± 16.5 years). This study investigated whether (i) NA, (ii) DTI changes, or (iii) ELH occur in VP, and (iv) to what extent said parameters relate. Methods included vestibulocochlear testing and MRI data analyses for neurovascular compression (NVC) and NA verification, DTI and ELS quantification. As a result, (i) NA increased NVC specificity. (ii) DTI structural integrity was reduced on the side affected by VP (p < 0.05). (iii) 61.1% VP showed mild ELH and higher asymmetry indices than NP (p > 0.05). (iv) “Disease duration” and “total number of attacks” correlated with the decreased structural integrity of the affected nerve in DTI (p < 0.001). NVC distance within the nerve’s root-entry zone correlated with nerve function (Roh = 0.72, p < 0.001), nerve integrity loss (Roh = − 0.638, p < 0.001), and ELS volume (Roh = − 0.604, p < 0.001) in VP. In conclusion, this study is the first to link eighth cranial nerve function, microstructure, and ELS changes in VP to clinical features and increased vulnerability of NVC in the root-entry zone. Combined MRI with NVC or NA verification, DTI and ELS quantification increased the diagnostic accuracy at group-level but did not suffice to diagnose VP on a single-subject level due to individual variability and lack of diagnostic specificity.
... Various methods have been proposed to qualitatively and quantitatively assess the endolymphatic space [10]. Most recent developments even allow the fully automatic 3D segmentation and Marc van Hoof, Raymond van de Berg have equally contributed to do this work volumetric quantification of the endolymphatic space [12,13], a significant step towards automatization and standardization of EH assessment on imaging. Nevertheless, EH is not a pathognomic feature to MD. ...
... Nevertheless, EH is not a pathognomic feature to MD. It is observed in various neuro-otologic pathologies as well as in asymptomatic ears [10][11][12]14]. The exact relationship between EH and MD and its pathological and clinical relevance are not completely understood. ...
Article
Full-text available
Purpose This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière’s disease. Materials and methods A retrospective, multicentric diagnostic case–control study was performed. This study included 120 patients with unilateral or bilateral Menière’s disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Menière’s disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model. Results The classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively. Conclusion The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Menière’s disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Menière’s disease.
... Second, ELS and perilymphatic space (PLS) were differentiated within the TFS using volumetric local thresholding (VOLT, [36]) that uses ImageJ Fiji [37], including the "Fuzzy and artificial neural networks image processing toolbox" [38] and the "MorphoLibJ Toolbox" [39]. The resulting 3D volume included ELS and PLS classifications for cochlea and vestibulum (cutoff 6). ...
Article
Full-text available
Knowledge of the physiological endolymphatic space (ELS) is necessary to estimate endolymphatic hydrops (ELH) in patients with vestibulocochlear syndromes. Therefore, the current study investigated age-dependent changes in the ELS of participants with normal vestibulocochlear testing. Sixty-four ears of 32 participants with normal vestibulocochlear testing aged between 21 and 75 years (45.8 ± 17.2 years, 20 females, 30 right-handed, two left-handed) were examined by intravenous delayed gadolinium-enhanced magnetic resonance imaging of the inner ear ( i MRI). Clinical diagnostics included neuro-otological assessment, video-oculography during caloric stimulation, and head-impulse test. i MRI data analysis provided semi-quantitative visual grading and automatic algorithmic quantitative segmentation of ELS volume (3D, mm ³ ) using a deep learning-based segmentation of the inner ear’s total fluid space (TFS) and volumetric local thresholding, as described earlier. As a result, following a 4-point ordinal scale, a mild ELH (grade 1) was found in 21/64 (32.8%) ears uni- or bilaterally in either cochlear, vestibulum, or both. Age and ELS were found to be positively correlated for the inner ear ( r (64) = 0.33, p < 0.01), and vestibulum ( r (64) = 0.25, p < 0.05). For the cochlea, the values correlated positively without reaching significance ( r (64) = 0.21). In conclusion, age-dependent increases of the ELS should be considered when evaluating potential ELH in single subjects and statistical group comparisons.
... Increasingly, attention is being directed to the inner ear, for example in decisions about cochlear implant candidacy and in patients with Ménière disease. [1][2][3] The inner ear has been difficult to image with MR imaging techniques due to its small size and environment, which includes a mixture of tissues of different proton densities, leading to susceptibility artifacts. 4 T2*-weighted sequences using either gradient-echo (eg, CISS) or FIESTA are used at many centers but are prone to banding artifacts. ...
Article
Background and purpose: MR imaging of the inner ear on heavily T2-weighted sequences frequently has areas of signal loss in the vestibule. The aim of the present study was to correlate the anatomic structures of the vestibule with areas of low signal intensity. Materials and methods: We reviewed T2-weighted spin-echo MR imaging studies of the internal auditory canal from 27 cases and cataloged signal intensity variations in the vestibulum of inner ears. Using a histologic preparation of a fully mounted human ear, we prepared 3D reconstructions showing the regions of sensory epithelia (semicircular canal cristae, utricular, and saccular maculae). Regions of low signal intensity were reconstructed in 3D, categorized by appearance, and compared with the 3D histologic preparation. Results: The region corresponding to the lateral semicircular canal crista showed signal loss in most studies (94%). In the utricle, a focus of signal loss occurred in the anterior-cranial portion of the utricle and corresponded to the location of the utricular macula and associated nerve on histopathologic specimens (63% of studies). Additional areas of low signal were observed in the vestibule, corresponding to the fluid-filled endolymphatic space and not to a solid anatomic structure. Conclusions: Small foci of signal loss within the inner ear vestibule on T2-weighted spin-echo images correlate with anatomic structures, including the lateral semicircular canal crista and the utricular macula. More posterior intensity variations in the endolymphatic space are likely artifacts, potentially representing fluid flow within the endolymph caused by magneto-hydrodynamic Lorentz forces.
... In order to eliminate the subjective (examiner's) bias, new automated images segmentation processing algorithms have been introduced (43,66), also associated with deep-learning models based on Artificial Intelligence (45). By adding the quantification of perilymphatic enhancement to the grading of EH, van Steekelenburg et al. (46) recently reported to improve the positive predictive value of Gd-enhanced MRI from 0.92 to 0.97 in the confirmation of definite MD. ...
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The diagnosis of “definite” Méniére's disease (MD) relies upon its clinical manifestations. MD has been related with Endolymphatic Hydrops (EH), an enlargement of the endolymphatic spaces (ES) (cochlear duct, posterior labyrinth, or both). Recent advances in Magnetic Resonance (MR) imaging justify its increasing role in the diagnostic workup: EH can be consistently recognized in living human subjects by means of 3-dimensional Fluid-Attenuated Inversion-Recovery sequences (3D-FLAIR) acquired 4 h post-injection of intra-venous (i.v.) Gadolinium-based contrast medium, or 24 h after an intratympanic (i.t.) injection. Different criteria to assess EH include: the comparison of the area of the vestibular ES with the whole vestibule on an axial section; the saccule-to-utricle ratio (“SURI”); and the bulging of the vestibular organs toward the inferior 1/3 of the vestibule, in contact with the stapedial platina (“VESCO”). An absolute link between MD and EH has been questioned, since not all patients with hydrops manifest MD symptoms. In this literature review, we report the technical refinements of the imaging methods proposed with either i.t. or i.v. delivery routes, and we browse the outcomes of MR imaging of the ES in both MD and non-MD patients. Finally, we summarize the following imaging findings observed by different researchers: blood-labyrinthine-barrier (BLB) breakdown, the extent and grading of EH, its correlation with clinical symptoms, otoneurological tests, and stage and progression of the disease.
... A follow-up and in-depth study of this population may shed some light on how this malaise may evolve, including EH. As a corollary of the previous findings, we stress our interest in not incorporating normal subjects into this study as the qualitative measurement we use probably could not be able to discriminate mild cochlear EH as do quantitative newer methods (49). ...
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Background: Endolymphatic hydrops (EH) is the histopathological hallmark of Ménière's disease (MD) and has been found by in vivo magnetic resonance imaging (MRI) in patients with several inner ear syndromes without definite MD criteria. The incidence and relevance of this finding is under debate. Purpose: The purpose of the study is to evaluate the prevalence and characteristics of EH and audiovestibular test results in groups of patients with fluctuating audiovestibular symptoms not fulfilling the actual criteria for definite MD and compare them with a similar group of patients with definite MD and a group of patients with recent idiopathic sudden neurosensory hearing loss (ISSNHL). Material and Methods: 170 patients were included, 83 with definite MD, 38 with fluctuating sensorineural hearing loss, 34 with recurrent vertigo, and 15 with ISSNHL. The clinical variables, audiovestibular tests, and EH were evaluated and compared. Logistic proportional hazard models were used to obtain the odds ratio for hydrops development, including a multivariable adjusted model for potential confounders. Results: No statistical differences between groups were found regarding disease duration, episodes, Tumarkin spells, migraine, vascular risk factors, or vestibular tests; only hearing loss showed differences. Regarding EH, we found significant differences between groups, with odds ratio (OR) for EH presence in definite MD group vs. all other patients of 11.43 (4.5–29.02; p < 0.001). If the ISSNHL group was used as reference, OR was 55.2 (11.9–253.9; p < 0.001) for the definite MD group, 9.9 (2.1–38.9; p = 0.003) for the recurrent vertigo group, and 5.1 (1.2–21.7; p = 0.03) for the group with fluctuating sensorineural hearing loss. Conclusion: The percentage of patients with EH varies between groups. It is minimal in the ISSNHL group and increases in groups with increasing fluctuating audiovestibular symptoms, with a rate of severe EH similar to the known rate of progression to definite MD in those groups, suggesting that presence of EH by MRI could be related to the risk of progression to definite MD. Thus, EH imaging in these patients is recommended.
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Objective: Intravenous contrast agent enhanced, high-resolution magnetic resonance imaging of the inner ear (iMRI) confirmed that patients with Menière's disease (MD) and vestibular migraine (VM) could present with endolymphatic hydrops (EH). The present study aimed to investigate EH characteristics and their interrelation to neurotologic testing in patients with VM, MD, or VM with concurrent MD (VM-MD). Methods: Sixty–two patients (45 females, aged 23–81 years) with definite or probable VM ( n = 25, 19 definite), MD ( n = 29, 17 definite), or showing characteristics of both diseases ( n = 8) were included in this study. Diagnostic workup included neurotologic assessments including video-oculography (VOG) during caloric stimulation and head-impulse test (HIT), ocular and cervical vestibular evoked myogenic potentials (o/cVEMP), pure tone audiometry (PTA), as well as iMRI. EH's degree was assessed visually and via volumetric quantification using a probabilistic atlas-based segmentation of the bony labyrinth and volumetric local thresholding (VOLT). Results: Although a relevant number of VM patients reported varying auditory symptoms (13 of 25, 52.0%), EH in VM was only observed twice. In contrast, EH in VM-MD was prevalent (2/8, 25%) and in MD frequent [23/29, 79.3%; χ ² (2) = 29.1, p < 0.001, φ = 0.7]. Location and laterality of EH and neurophysiological testing classifications were highly associated (Fisher exact test, p < 0.005). In MD, visual semi-quantitative grading and volumetric quantification correlated highly to each other ( r S = 0.8, p < 0.005, two-sided) and to side differences in VOG during caloric irrigation (vestibular EH ipsilateral: r S = 0.6, p < 0.05, two-sided). In VM, correlations were less pronounced. VM-MD assumed an intermediate position between VM and MD. Conclusion: Cochlear and vestibular hydrops can occur in MD and VM patients with auditory symptoms; this suggests inner ear damage irrespective of the diagnosis of MD or VM. The EH grades often correlated with auditory symptoms such as hearing impairment and tinnitus. Further research is required to uncover whether migraine is one causative factor of EH or whether EH in VM patients with auditory symptoms suggests an additional pathology due to MD.
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Though traditional thresholding methods are simple and efficient, they may result in poor segmentation results because only image’s brightness information is taken into account in the procedure of threshold selection. Considering the contextual information between pixels can improve segmentation accuracy. To to this, a new thresholding method is proposed in this paper. The proposed method constructs a new two dimensional histogram using brightness of a pixel and local relative entropy of it’s neighbor pixels. The local relative entropy (LRE) measures the brightness difference between a pixel and it’s neighbor pixels. The two dimensional histogram, consisting of gray level and LRE, can reflect the contextual information between pixels to a certain extent. The optimal thresholding vector is obtained via minimizing cross entropy criteria. Experimental results show that the proposed method can achieve more accurate segmentation results than other thresholding methods.
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Purpose Bilateral vestibulopathy (BV) is an uncommon disorder and the etiology remained idiopathic in most cases. Delayed 3D-FLAIR sequences have provided new insights into various inner ear diseases, allowing the evaluation of the endolymphatic space and the permeability of the blood–labyrinthine barrier (BLB). The aim of this study was to assess both the morphology of the endolymphatic space and the permeability of the BLB in patients with BV as evaluated by delayed 3D-FLAIR sequences. Methods In this retrospective study, we performed 3D-FLAIR sequences 4 h after administering contrast media to 42 patients with BV. Two radiologists independently evaluated the morphology of the endolymphatic space (either vestibular atelectasis or endolymphatic hydrops) and the permeability of the BLB. Results Morphologic anomalies of the endolymphatic space and vestibular blood–labyrinthine barrier impairment were observed in 59.6% of patients with BV. Bilateral vestibular atelectasis (VA) was found in 21 patients (50%), involving only the utricle and all three ampullas while the saccule was always observed with no sign of collapse: idiopathic BV (n = 19), aminoglycoside administration (n = 1) and few days following abdominal surgery (n = 1). One patient had bilateral vestibular malformation. BLB impairment was observed in five patients (11.9%): paraneoplastic (n = 1), lymphoma (n = 1), autoimmune (n = 1), and vestibular “neuritis” (n = 2). Seventeen patients (40.4%) had normal MRI with no endolymphatic space anomaly or BLB impairment. Conclusion Patients with BV presented with morphologic anomalies of the endolymphatic space or BLB impairment in 59.6% of patients.
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Purpose of review: To provide an update on the most frequent peripheral vestibular disorders. Recent findings: The on-going classification of vestibular disorders by the Bárány Society represents major progress. The diagnosis of bilateral vestibulopathy (BVP) requires quantitative testing of vestibular function. 'Acute unilateral peripheral vestibulopathy' (AUPVP) is now preferred over 'vestibular neuritis.' Menière's disease is a set of disorders with a significant genetic contribution. The apogeotropic variant of horizontal canal benign paroxysmal positional vertigo (hcBPPV) and anterior canal BPPV (acBPPV) can be distinguished from a central vestibular lesion. Vestibular paroxysmia is now an internationally accepted clinical entity. The diagnosis of SCDS is based on conclusive findings. Summary: Diagnosis of BVP requires significantly reduced vestibular function. The clinical picture of AUPVP depends on how much the vestibular end organs or their innervation are affected. Menière's disease phenotype is a constellation of symptoms. Although diagnostic and therapeutic criteria for pc and hcBPPV are well defined, a number of less frequent and controversial are increasingly diagnosed and can be treated. Diagnosis of vestibular paroxysmia requires that a patient responds to treatment with a sodium channel blocker. The diagnosis of SCDS requires conclusive findings with various methods. There is still a great need for state-of-the-art randomized controlled treatment trials in most peripheral vestibular disorders.
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Objectives: To review the clinical records of patients that exhibited the clinical features of both vestibular migraine (VM) and Ménière's disease (MD) during each episodic vertigo attack and to discuss the possible pathophysiology of such combination of symptoms. Subjects: Ten patients that were selected according to criteria based on a combination of the diagnostic criteria for definite MD and VM (9 females and one male, age: 22–54 years) were enrolled. They were required to show features of both diseases in each vertigo attack. Methods: The patients' medical histories and pure-tone audiometry, cervical vestibular evoked myogenic potential (cVEMP), video head-impulse test (vHIT), and caloric test results were examined. cVEMP was recorded using 500 and 1,000 Hz short tone bursts (125dBSPL, air-conducted), 500 Hz-1,000 Hz cVEMP slope, an index of endolymphatic hydrops in the saccule was calculated using normalized amplitudes of p13-n23. For performing vHIT, each subject was seated 1.5 m in front of a target and asked to keep watching it as their head was passively rotated by the examiner. Their eye movements were evaluated using video-oculography while their head movements were recorded using inertial sensors. Results: The patients were predominantly female. On average, the onset of migrainous headaches occurred 9 years earlier than the onset of vertigo attacks. All of the patients but one had migraines with auras. Five of the 10 patients had a family history of vertigo attacks accompanied by both migrainous and auditory symptoms. The patients mainly displayed hearing loss at low frequencies. Nine patients exhibited 500–1,000 Hz cVEMP slope < −19.9, which was suggestive of endolymphatic hydrops. None of the patients who underwent vHIT showed abnormal canal function. One patient showed unilaterally decreased caloric responses. Conclusions: These patients presented with simultaneous MD and VM signs/symptoms might be referred to “VM/MD overlapping syndrome (VM/MD-OS)” as a new clinical syndrome.
Article
Objectives: The purpose of this study was to investigate the grades of endolymphatic hydrops determined by gadolinium-contrast magnetic resonance (MR) and correlation to the clinical features in patients with Meniere disease. Study design: Prospective study. Methods: A total of 24 patients suffering from unilateral Meniere disease with either definite or probable clinical diagnosis were included. The duration of vertigo, duration of tinnitus, duration of vertigo attacks, hearing thresholds, and canal paresis (CP) value of caloric tests were assessed. Three-dimensional fluid-attenuated inversion recovery magnetic resonance imaging (MRI) was performed 4 hours after intravenous injection of double dose of gadobutrol (Gd) to show endolymph and perilymph, and the grades of endolymphatic hydrops were measured. Additionally, the correlation between clinical features and the grades of endolymphatic hydrops of cochlea and vestibular were evaluated. Results: Different grades of the endolymphatic hydrops in the impaired ear were revealed by MRI. The Spearman correlation showed a strong correlation between the hearing thresholds of low, middle, and high tone and the grades of cochlea and vestibular hydrops (P < .05); However, no significant correlation between the duration of vertigo, duration of tinnitus, duration of vertigo attacks, CP value, and endolymphatic hydrops was determined (P > .05). Conclusion: By visualizing the endolymph and perilymph of inner ear in patients with Meniere disease assisted with intravenous injection of double doses of Gd, the grades of endolymphatic hydrops could be assessed. As a result, the grades of endolymphatic hydrops in patients with Meniere disease can be used to predict the level of hearing impairment. Level of evidence: 4 Laryngoscope, 2020.
Article
Background: Delayed 3D-FLAIR sequences enable the distinction between the utricle and the saccule. Aims/objectives: We sought to evaluate the clinical and radiological findings in patients with no visible saccule (NVS) on 4-hour post-contrast MRI. Material and Methods: We retrospectively assessed the presence of NVS signs in 400 patients who underwent delayed inner ear MRI. Results: We reported on 28 patients with NVS. Among this group, on the NVS affected side: 14 had isolated sensorineural hearing loss (SNHL); 4 had fluctuating cochleo-vestibular disease; 3 had definite Menière’s disease; 3 had Minor syndrome; 2 had delayed endolymphatic hydrops (EH); 2 had inner ear malformations; 1 had sudden cochleo-vestibular deficit following stapes surgery; 1 had a perilymphatic fistula and 1 had a contralateral fluctuating SNHL. Sixteen out of these 28 patients (57.1%) had cochlear hydrops on the same side as the NVS, while 10 patients (35.7%) had saccular hydrops on the contralateral side. Moreover, isolated blood labyrinth barrier (BLB) impairment on the NVS side was observed in 7 patients. Two patients (7.1%) had large vestibular aqueduct and NVS on the same side and one patient had perilymphatic fistula. Conclusions and significance: NVS seems to be multifactorial and could be linked to hydropic ear disease, third-mobile window pathologies and congenital malformation.
Article
Objective: Endolymphatic hydrops (EH) has been reported in ears with otosclerosis. The objective of this study was to investigate the clinical features of ears with otosclerosis and EH on magnetic resonance imaging (MRI) and identify predictors for the presence of EH. Study design: Retrospective study. Setting: University hospital. Materials and methods: Forty-six ears from 37 patients with otosclerosis were included in the present study. Interventions: The subjects were divided into three groups, those with no, mild, or significant EH, based on 3-T MRI with intravenous injection of gadolinium. Hearing levels and the extent of otosclerotic lesions graded based on the computed tomography (CT) findings were compared among the groups. Moreover, to examine the vascular activity of the disease, intraoperative measurements of blood flow were also evaluated. Main outcome measures: Imaging, hearing levels, and blood flow values. Results: The overall rate of EH was 58.7% (27 of 46 ears); cochlear EH (52.2%) was more frequent than vestibular EH (26.1%). Average thresholds in ears with significant EH were significantly higher at several frequencies, both on air and bone conduction, than those with no or mild EH. Significant EH was more frequently observed in ears with advanced stages on CT than in those without advanced stages. The values of blood flow in the area anterior to the oval window were higher in some ears with EH than in ears without EH. Conclusion: EH was frequently present in ears with otosclerosis, especially those with severe hearing loss or advanced disease on CT.
Article
The diagnostic criteria for Meniere Disease (MD) are clinical and include two categories: definite MD and probable MD, based on clinical examination and without the necessity of advanced vestibular or audiological testing. The condition is a heterogeneous disorder and it is associated with endolymphatic hydrops (EH), an accumulation of endolymph in the inner ear that causes damage to the ganglion cells. Patients with suspected EH can be examined by Magnetic Resonance Imaging (MRI), offering new insights into these inner ear disorders. Results of imaging studies using the hydrops protocols show conflicting results in MD patients. These discrepancies can be dependent either on the MRI sequence parameters or on the method of hydrops grading or the inclusion criteria to select patients. The visualization of EH can be classified based on a semi-quantitative ratio between endolymph and perilymph liquids, or on the distinction between the saccule and the utricle structures. In addition, MRI can also be used to evaluate whether cochleovestibular nerves can present with imaging signs of axonal loss.In this systematic review, we have selected case-controlled studies to better characterize the potential added value in the diagnosis and management of patients with MD. Using different techniques, studies have identified the saccule as the most specifically involved structure in MD, and saccular hydrops seems to be associated with low to medium-tone sensorineural hearing loss degree. However, early symptoms still appear too subtle for identification using MRI and the reproducibility of the hydrops protocols with various MRI scan manufacturers is debatable, thus limiting expansion of these techniques into clinical practice for the diagnosis of MD at this time.Further research is needed. The future inclusion of semicircular canal hydrops location in the imaging signs and the application of MRI in patients with atypical presentations hold promise.
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Background: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by other researchers or clinicians. Even if developers publish their code and pre-trained models on the internet, integration in stand-alone applications and existing workflows is often not straightforward, especially for clinical research partners. In this paper, we propose an open-source framework to provide AI-enabled medical image analysis through the network. Methods: TOMAAT provides a cloud environment for general medical image analysis, composed of three basic components: (i) an announcement service, maintaining a public registry of (ii) multiple distributed server nodes offering various medical image analysis solutions, and (iii) client software offering simple interfaces for users. Deployment is realized through HTTP-based communication, along with an API and wrappers for common image manipulations during pre- and post-processing. Results: We demonstrate the utility and versatility of TOMAAT on several hallmark medical image analysis tasks: segmentation, diffeomorphic deformable atlas registration, landmark localization, and workflow integration. Through TOMAAT, the high hardware demands, setup and model complexity of demonstrated approaches are transparent to users, who are provided with simple client interfaces. We present example clients in three-dimensional Slicer, in the web browser, on iOS devices and in a commercially available, certified medical image analysis suite. Conclusion: TOMAAT enables deployment of state-of-the-art image segmentation in the cloud, fostering interaction among deep learning researchers and medical collaborators in the clinic. Currently, a public announcement service is hosted by the authors, and several ready-to-use services are registered and enlisted at http://tomaat.cloud.
Article
Spontaneous intracranial hypotension (SIH), which is caused by cerebrospinal fluid (CSF) leakage, is a rare pathology with annual incidence at 5 per 100 000 of the population.¹ Patients with SIH report orthostatic headache, dizziness, hearing disturbance, nausea and vomiting, cervical pain, and other symptoms. Ménière disease (MD) is a common inner ear disease characterized by recurrent vertigo, fluctuating hearing loss and tinnitus, and histopathologically, is associated with endolymphatic hydrops (EH). The key symptom of SIH is orthostatic headache, but SIH can be misdiagnosed as MD owing to similarities in clinical presentation. The cochleovestibular symptoms in patients with SIH are speculated to be related to EH in connection with CSF leakage.²,3 However, to our knowledge, no previous SIH patient with EH has been reported.