Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep

Apnea Analysis

Muhammad Laiq Ur Rahman Shahid

, Teodora Chitiboi

1,2

, Tatyana Ivanovska

, Vladimir

Molchanov

, Henry V

olzke

, Horst K. Hahn

1,2

and Lars Linsen

Jacobs University, Bremen, Germany

Fraunhofer MEVIS, Bremen, Germany

Ernst-Moritz-Arndt-Universit

at Greifswald, Greifswald, Germany

Keywords:

Obstructive Sleep Apnea (OSA), Pharynx Segmentation, Magnetic Resonance Imaging (MRI).

Abstract:

Obstructive sleep apnea (OSA) is a public health problem. Volumetric analysis of the upper airways can help

us to understand the pathogenesis of OSA. A reliable pharynx segmentation is the ﬁrst step in identifying the

anatomic risk factors for this sleeping disorder. As manual segmentation is a time-consuming and subjective

process, a fully automatic segmentation of pharyngeal structures is required when investigating larger data

bases such as in cohort studies. We develop a context-based automatic algorithm for segmenting pharynx

from magnetic resonance images (MRI). It consists of a pipeline of steps including pre-processing (thresh-

olding, connected component analysis) to extract coarse 3D objects, classiﬁcation of the objects (involving

object-based image analysis (OBIA), visual feature space analysis, and silhouette coefﬁcient computation) to

segregate pharynx from other structures automatically, and post-processing to reﬁne the shape of the identiﬁed

pharynx (including extraction of the oropharynx and propagating results from neighboring slices to slices that

are difﬁcult to delineate). Our technique is fast such that we can apply our algorithm to population-based

epidemiological studies that provide a high amount of data. Our method needs no user interaction to extract

the pharyngeal structure. The approach is quantitatively evaluated on ten datasets resulting in an average of

approximately 90% detected volume fraction and a 90% Dice coefﬁcient, which is in the range of the inter-

observer variation within manual segmentation results.

1 INTRODUCTION

Sleeping disorders such as obstructive sleep apnea

are public health problems affecting, at least, 2-4%

of the middle-aged population (Pack, 2002). Sleep-

disordered breathing (SDB) such as obstructive sleep

apnea not only causes poor sleep quality and daytime

sleepiness, but also has clinical consequences includ-

ing hypertension and increased risk of cardiovascu-

lar disease (CVD) (Shaw et al., 2008). SDB is in-

creasingly considered as a potential therapeutic target

for either primary or secondary prevention of cardio-

vascular disease. Obstructive sleep apnea is deﬁned

as a recurrent cessation of respiration associated with

an upper airway obstruction during sleep (Lowe and

Fleetham, 1991). It is marked by repeated episodes

of upper airway collapse, leading to apnea (cessation

of airﬂow for greater or equal to 10 seconds) or hy-

popnea (decrease in airﬂow for greater or equal to 10

seconds) (Berry et al., 2012). The clinical syndrome

of sleep apnea is deﬁned as the presence of abnor-

mal breathing in sleep along with daytime symptoms,

particularly excessive daytime sleepiness. Obstruc-

tive sleep apnea also results in impaired sleep qual-

ity leading to deterioration of memory and judgment,

altered personality, and reduced concentration (Shi

et al., 2006). Patients complain of a range of symp-

toms and may develop physical complications that in-

clude systematic hypertension, right heart failure, and

cardiac arrhythmias (Lowe et al., 1986).

While the clinical symptoms of obstructive sleep

apnea are well recognized, the understanding of its

pathogenesis remains incomplete. It is clear, however,

that upper airway anatomy is important in the patho-

genesis of obstructive sleep apnea. In order to fully

understand upper airway anatomy, we need to exam-

ine the volume of the airway and surrounding upper

airway structures (Ivanovska et al., 2013). To study

upper airway anatomy, it is important to develop a

segmentation technique to extract the anatomy from

599

Shahid M., Chitiboi T., Ivanovska T., Molchanov V., Völzke H., Hahn H. and Linsen L..

Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis.

DOI: 10.5220/0005315905990608

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 599-608

ISBN: 978-989-758-089-5

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

medical images.

Although, the X-ray computed tomography (CT)

is considered to be the gold standard for airways

imaging, its employment in an epidemiological co-

hort or repeated CT scans for research purposes in a

single patient are ethically not justiﬁed. Here, mag-

netic resonance imaging as anon-radiation based ex-

amination method gains an increasing popularity in

the clinical and epidemiological setting. MR imaging

offers a good contrast between soft tissue structures

and is utilized to analyse throat tissues (Ivanovska

et al., 2011). Because MRI involves little or no risk

to healthy volunteers, it makes it possible to perform

large-scale studies to understand the role of pharynx

morphology in sleep apnea.

The ﬁrst step towards this endeavour is to estab-

lish a reliable segmentation of the pharynx from 3D

MR images. As manual segmentation is a labori-

ous, observer-dependent, and time-consuming pro-

cess, full automation of the three-dimensional anal-

ysis of the pharynx is required. In this paper, we

present a fast and fully automatic segmentation ap-

proach for the pharynx from MR images based on

a pipeline of steps. We mainly apply a few pre-

processing steps to extract coarse segmentation of

candidates, extract their properties within a multidi-

mensional feature space, to which we apply a visual

analysis approach to ﬁnd a reliable classiﬁcation, and

reﬁne the shape of the identiﬁed pharynx in a post-

processing step. The results are analysed and com-

pared against manually established ground truth and

the state of the art. We can show that our automatic

method achieves results within the range of inter-

observer variability.

The rest of the paper is organized as follows: We

give an overview of related work in Section 2. The

material and data used in this study are described in

Section 3. Section 4 describes the working of our seg-

mentation method. The results are analyzed and com-

pared against manually established ground truth and

state-of-the-art techniques in Section 5 while Section

6 concludes the paper.

2 RELATED WORK

Since the X-ray CT is used more commonly for air-

ways imaging, most of the segmentation methods de-

signed for airways are applied to this imaging modal-

ity. Nearly all methods of segmentation and measure-

ment designed for airways published in literature are

developed for X-ray computed tomography and cone-

beam computed tomography images. However, due to

large differences between CT and MR images, those

methods cannot be applied for our purposes.

There are few publications dealing with the spe-

ciﬁc challenges of pharynx classiﬁcation and segmen-

tation from MRI data. Schwab et al. (2003) utilized

volumetric measurements of upper airways to ana-

lyzes anatomic alterations. The measurements were

performed manually. Liu et al. (2003) performed a

study to diagnose upper airway disorders in children

by delineating upper airway and surrounding struc-

tures with magnetic resonance imaging. They pro-

posed a semi-automatic framework for upper airway

segmentation using fuzzy connectedness. Here, T1-

and T2-weighted MR images were acquired and an

operator speciﬁed a volume of interest and seeds in

both image volumes manually. The mean processing

time is about four minutes including the operator in-

teraction. The method is based on a local fuzzy rela-

tion on voxels called afﬁnity, which indicates how the

voxels belong together locally within the scene in the

object of interest. Finally, a global fuzzy connected-

ness relation is constructed that decides how the vox-

els dangle together globally in the scene to form the

object. Andrysiak et al. (2001) reported a method

for upper airways analysis in patients with obstruc-

tive sleep apnea by using MRI and performed mea-

surements based on individual slices such as assessing

surfaces of the smallest cross-section of upper airway

lingual, thickness of soft palate, and the smallest dis-

tance between soft palate and throat wall.

Ivanovska et al. (2013) presented a pipeline for

pharynx segmentation with semi-automatic initializa-

tion. The automatic part of the approach consists of

three steps: smoothing, thresholding, and 2D and 3D

connected component analysis. Whereas the two ﬁrst

steps are rather common, the third step provides a set

of general rules for extraction of the pharyngeal com-

ponent. Their method requires less than one minute

to extract the pharyngeal structures. The approach

is mostly automatic, but uses a small amount of in-

teractions like deﬁning starting and ending slice of

oropharynx and marking the parapharyngeal fat pads

in them.

In conclusion, over the past years we have seen

important advances in upper airway MRI segmenta-

tion, prompted by an increase in the quality and avail-

ability of MR imagery. There has been a little work

towards developing an automatic segmentation tech-

nique. Some researchers tried to tackle this problem,

but it has not yet been completely solved.

3 MATERIALS

The test datasets were acquired in the frame of the

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Study of Health in Pomerania(SHIP) (V

olzke et al.,

2010), a population-based study conducted in North-

east Germany, where more than 2000 participants

aged 20 to 89 years were examined. The test datasets

are axial T1-weighted isotropic head MR images with

spatial resolution of 1mm in each dimension leading

to 176×256 ×176 voxels. These MR images contain

only the upper part of pharyngeal structures. How-

ever, the medical experts considered the images to

contain the most essential information and to be suit-

able to study the obstructive sleep apnea syndrome in

the frame of an epidemiological study.

We randomly select sixteen individual head MRI

datasets for our experiments and tests. To consider

the observer variability and error, two medical ex-

perts provide manually segmented ground truths for

ten datasets under the supervision of an experienced

radiologist.

4 PHARYNX SEGMENTATION

The large intensity variations and low contrast in MRI

make pharynx segmentation more challenging than in

CT images. Also, identifying the pharynx in individ-

ual axial MRI slices poses additional challenges be-

cause of its irregular shape and diverse appearance as

shown in Figure 1, rendering shape-based classiﬁca-

tion a difﬁcult task.

Figure 1: Diverse Appearance of Pharynx in Axial Slices.

State-of-the-art approaches rely on manually

placed markers to perform guided 3D image grow-

ing on a pixel level. However, when operating exclu-

sively on a voxel level, the spatial relation between the

pharynx and other neighbouring anatomical regions

is more difﬁcult to establish. Nevertheless, the local

context could provide useful information for identify-

ing our target structure. Thus, we overcome the limi-

tations of voxel-based processing by analyzing atomic

image regions (called objects) in their local semantic

context. Image regions possess many features that are

not available on a voxel level such as shape, orienta-

tion, intensity statistics, and relative position to other

regions. In our approach, we generate a set of 3D

image regions by over-segmentation, which are then

classiﬁed to detect the segments forming the pharynx.

The complete pipeline of our approach is shown

in Figure 2. First, the image is partitioned into coarse

3D regions using thresholding and connected compo-

nent analysis. The features describing the appearance

and orientation of each 3D region are then extracted,

which form a multidimensional feature space. By vi-

sually exploring the feature space we ﬁnd a combi-

nation of settings of pharynx characteristics, which

distinguishes it from other candidate objects. A clas-

siﬁer that detects the 3D pharynx samples in the mul-

tidimensional feature space is deﬁned. In a post-

processing, the pharynx region is slice-wise reﬁned.

After removing false surrounding voxels, the segment

of the pharynx which is thought to play a key role

in sleep apnea analysis is automatically extracted us-

ing central differences. Our approach is completely

automatic, requiring no user interaction. The results

were validated on sixteen datasets using two manual

ground truth segmentations. The following subsec-

tions provide the details on the individual steps of the

pipeline.

Figure 2: Complete pipeline.

AutomaticPharynxSegmentationfromMRIDataforObstructiveSleepApneaAnalysis

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4.1 Pre-processing

In most classiﬁcation and segmentation problems,

pre-processing plays a vital role. In our ap-

proach, we perform smoothing with a median and an

anisotropic ﬁlter followed by clustering using thresh-

olding (Shapiro and Linda, 2002).

First, we apply a median ﬁlter of kernel size

3 × 3 × 5 to remove isolated voxels and minimize the

salt and pepper noise (Gonzalez and Woods, 2008).

After this, we smooth the image using the anisotropic

diffusion ﬁlter (Perona and Malik, 1990),which can

cope with the low intensity contrast and intensity vari-

ations in MRI sequences, while preserving the bor-

ders intact.

Finally, we apply a coarse intensity clustering to

the image volume to reduce the computational com-

plexity, and extract coarse and homogeneous regions

in each image slice by selecting half of the mean gray

value as threshold value. By thresholding, we select

the darkest regions in the image, as we know that the

pharyngeal air column is one of the darkest classes in

MR images. The thresholding step signiﬁcantly de-

creases the number of over-segmented objects in the

head MR image volume. Figure 3 shows an axial slice

of the MRI dataset, where the dark objects remaining

as candidates for the pharynx are highlighted.

Figure 3: 3D object candidates for the pharynx shown on

axial slice.

At the end of the pre-processing step, we are left

with dark regions on axial slices of the MRI datasets

which includes the pharyngeal air pipe and other dark

regions like vertebrae, teeth, and bone structures.

4.2 Object-based Image Analysis

Having dark regions on axial slices, the next task is to

connect them using connected component analysis in

order to build 3D objects. During connected compo-

nent analysis, we use a 26 neighborhood relationship

in 3D to connect dark region voxels to build 3D coarse

objects in MRI dataset. In the nasopharynx region,

we face a region leakage problem due to low inten-

sity contrast, which makes the pharynx shape more

irregular and random. Thus, we need to exclude the

nasopharynx level from the 3D coarse objects during

connected component analysis, such that we are left

with a more regular and well-deﬁned shape for the

pharynx. We achieve this task by separating the na-

sopharynx region from the oropharynx where the ax-

ial area of the pharynx exceeds a certain limit.

Daniel et al. (2007) mentioned average dimen-

sions of the pharyngeal air column in both genders.

We also measure the area of the pharynx on each ax-

ial slice for our datasets and ﬁnd the maximum values

which are shown in Table 1. After a complete analy-

sis of the pharynx area on axial slices, we break the

linkage between nasopharynx and oropharynx where

the pharynx has a physical dimension greater than a

threshold value (350 mm

) on any axial slice. In ad-

dition to this, the total volume of the visible pharyn-

geal part is 2000 mm

to 4000 mm

on average in our

datasets. Therefore, we can discard 3D objects which

have a volume less than 400 mm

to reduce the num-

ber of 3D coarse objects. This additional step further

decreases the number of remaining 3D objects. Fig-

ure 3 shows that after performing all these tasks, only

few 3D objects are left as pharynx candidates in the

object-based image analysis step, which makes our

algorithm more time efﬁcient.

Table 1: Maximum area of pharynx on any axial slice.

MRI Head Dataset

Maximum Area of Pharynx

on Axial Slice in mm

283

198

211

252

192

275

255

169

264

303

223

209

204

210

257

235

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4.3 Visual Analysis of Feature Space

After building 3D objects and eliminating the redun-

dant objects, our task is to classify them with re-

spect to some features. Hence, we obtain the de-

scriptive features of atomic image regions using a

generic framework for object-based image analysis

introduced by Homeyer et al. (2010). The image re-

gions are described by a set of features regarding their

shape and intensity statistics.

The intensity features comprise of standard statis-

tics over the 3D image region, including minimum,

maximum, and average values as well as median and

upper and lower quartiles.

Regarding the regions shape, there are many de-

scriptors available in literature as shown in the sur-

vey by Mingqiang et al. (2008). One of the standard

methods to compute shape features is based on central

image moments (Burger and Burge, 2009) of the bi-

nary mask of a region r, where r(x, y) = 1 if the voxel

(x, y) belongs to the region and r(x, y) = 0 otherwise.

The central image moments of the order p, q for a bi-

nary image r(x, y) are deﬁned by

(r) =

∑

(x − x)

(y − y)

r(x, y)

where (x, y) represents the coordinates of the cen-

troid and the zero-order moment represents the area

of a binary image. Furthermore, we perform princi-

pal component analysis (Jolliffe, 2005) on the voxel

distribution of the region. The principal eigenvectors

and corresponding eigenvalues λ1 and λ2 can be com-

puted from the covariance matrix of r, which is de-

ﬁned by

cov(r) =



(r) µ

(r)

(r) µ

(r)



The principal eigenvectors deﬁne the regions ori-

entation, while the ratio of their corresponding eigen-

values measures the regions eccentricity. Using cen-

tral moments, we can derive different shape features.

We analysed the histograms of different individual

features for pharynx and other 3D objects. It is clear

from the histograms in Figure 4 that the chosen sin-

gle features do not sufﬁce to distinguish the pharynx

from other objects. The same holds for other features.

Nevertheless, pharynx objects have a narrow range of

values for some speciﬁc features which can be used

in a combination with other features to separate them

from other false candidates.

The shape and statistical features form a multi-

dimensional feature space for pharynx description.

Hence, we generate a set of potentially descriptive

Figure 4: Histograms of individual features. Top: X-axis

values of centers of 3D objects. Bottom: Principal Y-axis

values of 3D objects.

features for our 3D coarse objects in our MRI head

datasets. Our next task is to determine which com-

bination of features best distinguishes the subset of

objects that belongs to the pharynx.

After generating a set of possible descriptive fea-

tures for our 3D coarse objects, we visualize the in-

ﬂuence of those features using histograms to ﬁnd the

most relevant features which segregate pharynx sam-

ples from remaining samples as shown in Figure 4.

It is found that principal eigenvectors and eigenval-

ues have compactness and few variations for pharynx

samples. So, we visually analyse our feature space

and ﬁnd suitable values of our features to separate

pharynx from other tissues in MRI dataset.

We use star-coordinates widget (Molchanov and

Linsen, 2014) to explore multidimensional feature

space. To visually explore the data distribution, we

apply the dimensionality reduction method such as

linear projection method to map multidimensional

data to a 2D space. Linear projection is the most eco-

nomical method in terms of computational cost and it

doesn’t introduce too much distortion which makes it

most suitable choice for our purpose. The linear pro-

jection of our 5-dimensional feature space onto the

2-dimensional visual space is deﬁned by a projection

AutomaticPharynxSegmentationfromMRIDataforObstructiveSleepApneaAnalysis

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matrix of size 2 × 5. We visualize the columns of our

projection matrix as axes of star-coordinates widget.

We use a training dataset, where we know the ground

truth in the form of manually segmented pharynx re-

gions and ﬁnd a conﬁguration of the star-coordinates

widget such that samples representing these pharynx

regions are visually decoupled from other regions as

demonstrated in Figure 5. It is very clear that the cho-

sen conﬁguration of the star-coordinates widget cre-

ates a projected space that exhibits an area with only

red samples representing pharynx. We observe that

it sufﬁces to select only ﬁve salient features, namely,

PCA descriptors (lambda1, lambda2, principal y-axis,

and principal z-axis) and x-center values (x-axis val-

ues for centers of objects) to create this projected

space as shown in Figure 5.

Figure 5: Top: Star-coordinate widget conﬁguration. Bot-

tom: Red and blue samples represent pharynx and non-

pharynx clusters, respectively, in projected view.

We store the recorded projection matrix for this

projected space from training datasets which serves as

a classiﬁer for test datasets. In order to apply the clas-

siﬁcation to test data, we map the new feature data us-

ing the recorded projection matrix. Then, we compute

the silhouette coefﬁcient for each candidate of the test

dataset considering the pharynx cluster of the train-

ing data. The best silhouette coefﬁcient, then, delivers

the region that is supposed to represent the pharynx in

the test data. The silhouette coefﬁcient (Rousseeuw,

1987) is deﬁned by

s(i) =

b(i) − a(i)

max{a(i), b(i)}

where s(i) is the value of the silhouette coefﬁcient

for the ith sample of the test dataset, We use Euclidean

distance as a measure of dissimilarity. We then deﬁne

a(i) as the average of the Euclidean distances from

ith sample to all samples of the pharynx cluster in the

training dataset, and b(i) as the average of the Eu-

clidean distances from the ith sample to all samples

of the non-pharynx cluster in the training dataset. All

pharynx and non-pharynx points of training datasets

are shown as red and blue points, respectively, in the

projected space of Figure 5.

On average, the total number of 3D segments in

each dataset is about 18, where only one segment is

classiﬁed to represent the pharynx. Detailed informa-

tion can be found in Table 2. To estimate how ac-

curately our pharynx classiﬁer performs in practice,

we use the leave-one-out cross validation technique

for the sixteen datasets. In a single round of cross-

validation, we use our ﬁfteen datasets as training set

and one dataset as testing set. To reduce the variabil-

ity, multiple rounds of cross-validation are performed

using different partitions, and the validation results

are averaged over the rounds.

Table 2: Total number of segments and statistics for phar-

ynx classiﬁer.

MRI Head Dataset Total number of 3D objects

1 11

2 20

3 21

4 15

5 21

6 18

7 20

8 15

9 23

10 12

11 19

12 23

13 15

14 14

15 17

16 19

Average 17.6

The result of the validation was the perfect result

of 100% true positive rate and zero false positives for

pharynx classiﬁcation, which serves as the basis for

our fully automatic pharynx segmentation algorithm.

In addition to it, we do not need any post-processing

step to decrease the false positive rate which makes

our classiﬁer more efﬁcient and economical in terms

of time and computational complexity.

4.4 Slice-wise Reﬁnement of Pharynx

Since coarse intensity clustering was applied to com-

plete MRI dataset in the pre-processing step to reduce

the computational complexity, a reﬁnement of the 3D

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object representing pharynx is necessary. Due to low

contrast and inhomogeneous intensity over the com-

plete MRI volume, but also because of performing

the segmentation on a lower intensity resolution, the

intermediate results include some false surrounding

voxels. This reﬁnement is performed on a slice-wise

basis in axial direction.

We start the reﬁnement step from the bottom slice

where hypopharynx and oropharynx meet each other.

For each slice, we ﬁnd the center of gravity of the

pharynx from the corresponding axial slice of the ini-

tial 3D coarse object representing the pharynx. There-

after, we select a region of radius r (where r is equal

to 2.5 times the radius of the oropharynx) around the

center of gravity which is still comparatively much

smaller than the entire axial slice. We select such a

value of r to make sure that our selected area must

consist of different intensity clusters (pharyngeal air

column, wall, and surrounding tissues) which help

MultiOtsu thresholding (Liao et al., 2001) to delineate

pharyngeal air column from the surrounding objects

perfectly. The darkest class in the center of the slice

represents the air column of the pharynx. In few ax-

ial slices of retro-palatal region, two or three disjoint

regions may represent the pharynx. In these slices,

we consider all dark regions lying within the radius

of the pharynx from the center of gravity of the ad-

jacent lower axial slice as a part of the pharynx. We

also update the center of gravity for the current slice.

Thereafter, we perform a morphological closing oper-

ation (Soille, 1999) to ﬁll the remaining holes and cor-

rect the boundary irregularities. After completing all

steps of slice-wise reﬁnement, the 3D pharynx object

is readily extracted for the analysis of the key region

of oropharynx that is believed to play an essential role

in sleep apnea.

4.5 Extraction of Retropalatal

Oropharynx

For obstructive sleep apnea syndrome analysis, we

need to extract the narrowest part of the oropharynx

from the retro-palatal region. Figure 6 shows the com-

plete structure of pharynx in red color for a typical

MRI dataset. However, not every acquired MRI vol-

ume is identically positioned with respect to the pa-

tient, such that the retro-palatal region can be located

at a variable position along the pharynx. Therefore,

we developed an automatic algorithm to identify and

extract the most important segment of the pharynx.

To achieve this task, we compute the central dif-

ferences of anteroposterior length of the pharyngeal

air column and ﬁnd the axial slice having the maxi-

mum value of central difference. Figure 7 shows the

Figure 6: Left: Lines 1 and 2 represent high and low retro-

palatal oropharynx region levels respectively. Right: Line

segments A and B represent the complete pharynx and the

retro-palatal region of oropharynx respectively.

anteroposterior length of the pharyngeal air column.

We notice that the starting low retro-palatal

oropharynx axial slice has the maximum central dif-

ference value due to the presence of soft palate which

is absent in the adjacent lower slice. This fact is

clearly shown in Figure 6. Moreover, we also con-

sider the condition that the anteroposterior length of

the pharyngeal air column should not be greater than

10mm (Daniel et al., 2007) to conﬁrm that we are op-

erating in the low retro-palatal region.

We can also ﬁnd a selection criterion for the last

slice of high retro-palatal oropharynx region where

it meets the nasopharynx region. When observing

a sudden change in the area of the pharynx, the na-

sopharynx region starts. We stop our process of in-

cluding axial slices as a part of the oropharynx when

the central difference of area becomes greater than 50

. In addition to this, we also consider the condi-

tion that the anteroposterior length of the pharyngeal

air column should be greater than 15 mm to conﬁrm

that we are operating in the high retro-palatal region.

By applying these criteria, we extract the retro-palatal

oropharynx region that is relevant to analyze the ob-

structive sleep apnea syndrome.

Figure 7: AP and LL line segments represent Anteropos-

terior and Laterolateral Dimensions of Pharyngeal Air Col-

umn respectively.

5 RESULTS AND COMPARISON

The proposed segmentation pipeline has been tested

AutomaticPharynxSegmentationfromMRIDataforObstructiveSleepApneaAnalysis

605

on ten datasets; each dataset represents a separate sub-

ject. The processing for each dataset with a resolution

176 × 256 × 176 takes less than half of a minute on a

computer with Intel Core i5 2.67 GHz CPU with 4 GB

RAM. The parameter settings have been preselected

and no additional parameter tuning is required. The

segmentation results are evaluated using four mea-

sures: false negative volume fraction (FNVF), false

positive volume fraction (FPVF), true positive volume

fraction (TPVF), and Dice coefﬁcient (DICE). These

metrics are based on voxel ratios. Let C

auto

and C

exp

denote the binary masks produced by our pipeline and

the expert delineation, correspondingly. Then FNVF,

FPVF, TPVF and DICE are deﬁned as follows

FNV F =

exp

−C

auto

exp

T PV F =

exp

∩C

auto

exp

FPV F =

auto

−C

exp

DICE =

2 ∗ |C

exp

∩C

auto

exp

| + |C

auto

These four metrics are calculated according to the

given formulas and presented in Table 3 so that au-

tomatic segmented results can be compared to the

expert-deﬁned ground truth for ten different datasets.

Higher values of DICE and TPVF fraction represent

better results, whereas lower values of FPVF and

FNVF represent better results. For ideal results, we

would have 100% results in terms of DICE and TPVF

fractions and 0% in terms of FPVF and FNVF frac-

tions. We compare the results to the semi-automatic

pharynx segmentation approach by Ivanovska et al.

(2013).

Table 3: Comparison of all masks against expert1s mask in

terms of four metrics mean values.

Masks

DICE TPVF FNVF FPVF

(%) (%) (%) (%)

Expert2 91.6 93.1 6.9 10.1

Ivanovska 89.4 85.8 14.2 6.3

Automatic 89.0 91.9 8.1 14.6

As it can be seen in Table 3, our approach pro-

duces results with high TPVF (mean value is around

92%) and low FNVF (mean value is close to 8%). The

Dice coefﬁcient is about 89%. However, the false

positive rate is about 15%, which can be partially

explained by the fact that we observed experts error

in delineating pharynx in the expert-deﬁned ground

truth. Therefore, we obtained another ground truth by

manual segmentation of a different medical expert for

the same subjects, in order to understand the amount

and character of variations better. We measure same

quality metrics for our segmentation results against

the second ground truth and present the values in Ta-

ble 4.

We also compare both ground truths against each

other using these four quality metrics and show the

intra-observer variability in Table 3 and Table 4.

Table 4: Comparison of all masks against expert2s mask in

terms of four metrics mean values.

Masks

DICE TPVF FNVF FPVF

(%) (%) (%) (%)

Expert1 91.6 90.4 9.7 6.8

Ivanovska 89.5 86.9 13.1 5.3

Automatic 88.7 93.0 7.0 17.0

The comparison of experts ground truth segmenta-

tions to the automatic results reveals the fact that man-

ual extraction of the pharynx is not much more accu-

rate than the automatic segmentation pipeline. The

human error is due to the voxels on the boundary of

pharyngeal air column which are affected by partial

volume. The ambiguous boundary emphasizes the

subjectivity of the human experts. In addition to this,

pharynx is present only partially in the MRI head vol-

ume, i.e., the pharyngeal parts of each subject differs

in size, which also might cause the variations in re-

sults. The total volume of the visible pharyngeal part

is quite low: on average it accounts to about 2,000

to 4,500 voxels, so minor variations such as 20 to

30 voxels per slice produce signiﬁcant false positive

or false negative errors. Moreover, our datasets are

subject to strong artefacts in the bottom slices. Fur-

thermore, low contrast and inhomogeneity artefacts of

datasets are main cause of high false positive errors.

A comparison with other existing pharynx seg-

mentation algorithms (other than Ivanovska et al.) is

difﬁcult due to different imaging modalities (X-ray

CT Scan and Cone-beam CT Scans) and inconsis-

tently used quality metrics. For example, Cheng et

al. (2007) proposed a pipeline for upper airways ex-

traction from the CT data, but no accuracy measures

were reported. Liu et al. (2003) described a semi-

automatic framework for MRI data and claimed a

higher accuracy for their approach. However, they

need more user involvement in the processing and

the total processing time is longer. Ivanovska et al.

(2013) also presented a semi-automatic pharynx seg-

mentation technique for MRI datasets, in which seed

points have to be selected manually. We calculated

these qualitative metrics for their technique and val-

ues are presented in Table 3 and Table 4. For easier

comparison of the results, we also show the graphs in

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications

606

Figure 8: Graph showing results against Expert 1 mask.

Figure 9: Graph showing results against Expert 2 mask.

Figure 8 and Figure 9.

Our results are comparable in terms of accu-

racy with the other approach. However, Ivanovskas

segmentation technique takes nearly more than one

minute on our system, i.e., our proposed technique

is about two times faster. Secondly, our technique

is fully automatic requiring no user input, while her

technique is semi-automatic.

Finally, the results of manual, semi-automatic and

fully automatic segmentation algorithms support our

argument that we are facing a limitation in practice

due to the subjectivity in the manual segmentation of

the pharynx. With our fully automatic method we aim

to address this problem and ensure accurate and re-

peatable segmentation results.

6 CONCLUSION AND FUTURE

WORK

We presented a fast and fully automatic segmen-

tation technique for pharynx extraction from head

MR images. The procedure consists of pre-

processing, object-based image analysis, and retro-

palatal oropharynx extraction. The method is fast,

as the whole processing takes less than half a minute

for one dataset. The approach has been tested on ten

random datasets. The proposed approach produces

sufﬁciently reliable and good results and has poten-

tial to be applied for the analysis of numerous data

in epidemiological studies such as SHIP to describe

the pathogenesis of the obstructive sleep apnea syn-

drome. Our approach could also be easily integrated

in clinical practice.

Our complete segmentation technique works well

for extracting pharynx and our main idea of segmenta-

tion in two steps can be applied in many medical prob-

lems especially for segmenting organs. The two-step

segmentation method consists of a coarse segmenta-

tion using intensity clustering as a ﬁrst step and reﬁne-

ment on local region using MultiOtsu thresholding as

second step after having identiﬁed the correct region

using a classiﬁcation in a multidimensional feature

space.

As future work, we plan to extend the algorithm

for segmentation of other soft tissues in the throat,

namely, fat pads. The main priority for future work

is to make the procedure as general as possible such

that it can be applied to different modalities and im-

ages.

ACKNOWLEDGEMENTS

We would like to thank the Higher Education Com-

mission (HEC) Pakistan and the University of En-

gineering and Technology Taxila, Chakwal campus,

Pakistan, and the DAAD for providing funding to

carry out this research to ﬁnd the pathogenesis of

Obstructive Sleep Apnea public health problem. We

are also thankful to the Visualization and Computer

Graphics Laboratory (VCGL) research group of Ja-

cobs University, Bremen, Germany, for valuable sug-

gestions and remarks. Finally, we are really grateful

to our medical experts team and other members from

the University of Greifswald, Germany, for their col-

laboration in this project.

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