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

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.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.

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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 [8–10] and
central [11–14] pathologies, as well as in anatomic or vas-
cular abnormalities affecting endolymph resorption [15–17].
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 [34–37]. 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
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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
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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 exibility–deep 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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