A Fully Automatic Tool for Counting Virchow-Robin Spaces in Magnetic

Resonance Imaging for Lacunar Stroke Study

ergio Pereira

, Jos

e A. Mariz

, Nuno Sousa

, Jos

e H. Correia

and Carlos A. Silva

Electronics Department, University of Minho, Campus Azur

em, Guimar

aes, Portugal

Life and Health Science Research Institute (ICVS), School of Health Sciences, University of Minho, Portugal

and ICVS/3B’s - PT Government Associate Laboratory, Braga/Guimar

aes, Portugal

Keywords:

Virchow-Robin Spaces, Lacunar Stroke, MRI, Medical Image Analysis, Automatic Tool.

Abstract:

Virchow-Robin Spaces surround the perforating arteries of the brain and sometimes they become dilated.

Studies suggest that those structures are correlated with some conditions such as lacunar strokes, small vessel

diseases, multiple sclerosis or even normal aging. However, the majority of those studies are based on the

detection of those structures by a human expert, in some regions of interest, which is prone to the subjectivity

of the person doing the task. Moreover, dilated Virchow-Robin Spaces may look similar to lacunar strokes,

making them difﬁcult to identify. Few works have been proposed on the computational detection of dilated

Virchow-Robin Spaces. In this paper, we propose a fully automatic tool, capable of preprocessing the magnetic

resonance images, extract the most relevant regions of interest and detect dilated Virchow-Robin Spaces. Such

a tool may be useful to eliminate human subjectivity, but also to improve the reproducibility of the studies,

leading to more reliable correlations. An application to visualize and count the detected structures was also

built, with the aim of helping in a study of the correlation of lacunar strokes, Virchow-Robin Spaces and

vascular dementia.

1 INTRODUCTION

Virchow-Robin Spaces (VRS) are the spaces, ﬁlled

with cerebrospinal ﬂuid (CSF) and intersticial ﬂuid,

between the perforating arteries of the brain and an

extention of the pia mater that surrounds the blood

vessel (Rouhl et al., 2008). Typically, VRS are micro-

scopic structures, but they can become dilated, which

makes them visible in magnetic resonance imaging

(MRI), with intensity near the CSF. However, the di-

mensions of dilated VRS are, usually, between 3 mm

and 15 mm for its length and up to 3 mm in diameter

(Descombes et al., 2004), which is near the resolu-

tion of the MRI scanners. So, the identiﬁcation of

these structures is difﬁcult not only for computational

methods, but also for physicians. In fact, they may be

mistakenly identiﬁed as lacunar strokes (Hern

andez

et al., 2013).

The importance of detecting dilated VRS is re-

lated with studies that suggest that there is a cor-

relation between the number of these structures and

some diseases or conditions, such as lacunar strokes

and other small vessel diseases (Doubal et al., 2010;

Rouhl et al., 2008); it can, also, be useful to distin-

guish vascular dementia from Alzheimer’s Disease

(Patankar et al., 2005). Additionally, it was found

a correlation between multiple sclerosis and the vol-

ume of dilated VRS, but not with its number (Wuerfel

et al., 2008). Those studies divided dilated VRS into

the regions where they were observed, being the most

common, and most relevant for the studies, the basal

ganglia and cerebral white matter (WM) (Patankar

et al., 2005; Doubal et al., 2010; Rouhl et al., 2008).

However, dilated VRS are also observed in healthy el-

derly people and, although less frequently, in younger

people, which makes some authors to argue that it is

just a feature of normal aging (Groeschel et al., 2006).

There are few works dedicated to computational

methods for the detection of dilated VRS. In (De-

scombes et al., 2004), the authors proposed an au-

tomatic method by modeling the geometry and spa-

tial distribution of dilated VRS with a Marked Point

Process, that was, then, optimized with a Reversible

Jump Markov Chain Monte Carlo (RJMCMC) algo-

rithm and simulated annealing, using the T1-weighted

MPRAGE sequence. Other authors applied a multi-

sequence approach with thresholds and connected re-

gions, but it had the disadvantage of being semiauto-

matic (Wuerfel et al., 2008). It is difﬁcult to differen-

tiate lacunar infarcts from VRS, even for specialists,

Pereira S., Mariz J., Sousa N., Correia J. and Silva C..

A Fully Automatic Tool for Counting Virchow-Robin Spaces in Magnetic Resonance Imaging for Lacunar Stroke Study.

DOI: 10.5220/0005199700690075

In Proceedings of the International Conference on Bioimaging (BIOIMAGING-2015), pages 69-75

ISBN: 978-989-758-072-7

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

Figure 1: Proposed pipeline.

so in (Uchiyama et al., 2008) it was proposed an auto-

matic method that extracted both, and then identiﬁed

VRS with a neural network classiﬁer. Other times

dilated VRS were treated as false positives, such as

in (Ramirez et al., 2011), where the objective was to

segment subcortical hyperintensities with an adaptive

threshold, and after that VRS were removed by delet-

ing small connected components.

A recent review on automatic assessment of di-

lated VRS (Hern

andez et al., 2013) concluded that

there are too few methods. It is pointed out that a

successful method would be multi-sequence, and it

should detect dilated VRS for different brain regions,

taking into account the intensity, shape, size, loca-

tion and spatial distribution. We believe that given

the clinical attention those structures are receiving, a

method for the automatic detection of dilated VRS

would be important, since it would lead to more re-

producible evaluation and more meaningful results.

In this paper, we propose a complete and fully au-

tomatic tool for the detection of dilated VRS, using

MRI, in two speciﬁc brain regions, the basal ganglia

and cerebral WM. The aim of the proposed tool is for

supporting a study on lacunar strokes and its corre-

lation with dementia. The pipeline comprises several

procedures, namely the correction of the bias ﬁeld,

skull stripping, extraction of regions of interest and

detection of dilated VRS. Finally, it was, also, devel-

oped an application to display the counting and to fa-

cilitate the analysis of the detected dilated VRS.

The remainder of this paper is organized as fol-

lows. In Section 2, we present the stages of the

pipeline and the main features of the graphical appli-

cation. We discuss results and show some images of

detected VRS and the graphical application in Section

3. Finally, we summarize the main contributions and

identify future developments in Section 4.

2 METHODS

The proposed tool has three main stages: preprocess-

ing, extraction of the region of interest (ROI) of the

brain, and detection and counting of dilated VRS.

Also, a graphical user interface was developed to per-

mit the visualization and inspection of the detected

structures (Figure 4). Figure 1 depicts the proposed

pipeline. Each stage comprises the execution of sev-

eral algorithms, whose implementation is found in

public neuroimaging packages or in-house solutions.

These commands are integrated in the pipeline us-

ing the software package Nipype (Gorgolewski et al.,

2011), allowing their automatic execution.

2.1 Preprocessing

Since our goal was to obtain an automatic tool that

could be applied right after the acquisition of the im-

ages, the ﬁrst stage aims to prepare the acquired im-

ages for segmentation.

We start by correcting the bias ﬁeld with the

N4ITK method (Tustison et al., 2010), which ensures

that the intensity of the same type of tissues stays uni-

form along the brain. This is important because we

use the intensities of the tissues to build ﬁlters, ana-

lyze VRS candidates and segment WM and gray mat-

ter (GM), in a further stage. After evaluating other al-

ternatives, we opted for the skull stripping procedure

distributed with Freesurfer (S

egonne et al., 2004); this

step is important for our tissue segmentation method.

As the last step in the preprocessing stage, we denoise

the image, since noise increases the false detection

of dilated VRS. We selected the rotationally invariant

(a) (b)

Figure 2: Effect of denoising. In a) can be observed the

original image, before denoising; the resulting image, with-

out noise is in b).

BIOIMAGING2015-InternationalConferenceonBioimaging

nonlocal means ﬁlter (Manj

on et al., 2012), which in

our tests preserved better the ﬁne details and had less

smoothing, which could compromise the detection of

dilated VRS, as can be observed in Figure 2.

2.2 Extraction of the Regions of Interest

In this stage, the basal ganglia and cerebral WM are

extracted.

2.2.1 Basal Ganglia

Basal ganglia comprises the caudate nucleus, puta-

men and globus pallidus. This region together with

the brainstem was segmented with the subcortical

segmentation method included in Freesurfer (Fischl

et al., 2002). The reason for the inclusion of the brain-

stem is that it contains substantia nigra, which is an

important region with penetrating arteries. However,

this ROI is commonly affected by strokes and microb-

leeds that sometimes result in holes in the segmenta-

tion. To cope with the incorrect segmentation, we ap-

ply the dilation morphological operator to close the

holes. This dilation permits, also, to extend the seg-

mentation to include the subinsular region, which is

an important region for dilated VRS. The basal gan-

glia of one subject in the axial plane, before and after

correction, can be observed in Figure 3.

(a) (b)

Figure 3: Basal ganglia segmented by freesurfer (a) and af-

ter correction (b).

2.2.2 Cerebral White Matter

To extract the cerebral WM, it was applied a method

developed in-house (Pereira et al., 2013) to segment

the brain into CSF, GM and WM. This segmentation

is performed by classifying each voxel according to

the tissue type. The classiﬁer is a Random Forest,

which exhibits important properties as being a multi-

class classiﬁer and having good generalization capa-

bilities for unseen data. Also, it is capable to handle

large feature vectors without overﬁtting, which per-

mits good characterization of the problem. As fea-

tures, we used the intensity of the voxel, measures

(mean, sum and median) in the axial plane centered

in the voxel under analysis with areas of 3, 9, 15 and

19 mm

, tissues posterior probabilities, tissues prob-

ability maps and the magnitude of the gradient.

As the segmentation is performed by classifying

each voxel individually, we may obtain isolated mis-

classiﬁed voxels. These are found and replaced by

the mode of the neighborhood. Finally, the subcorti-

cal segmentation obtained using Freesurfer is used to

remove the cerebellum and brainstem.

2.3 Detection of Dilated Virchow-Robin

Spaces

For detecting VRS, we propose an extension of the

method proposed by Descombes et al.(Descombes

et al., 2004), which we brieﬂy describe bellow, in-

dicating steps where we have diverged from, in our

implementation.

Given the geometry of dilated VRS, Descombes

modeled them as cylindrical structures with length be-

tween 3 and 15 mm and diameter between 1 and 3

mm. Since their intensity is similar to the CSF and

they are surrounded by normal tissue, whose inten-

sity is higher and contrasts with the VRS, the image

is ﬁltered in each plane with three ﬁlters. This ﬁl-

tering aims to enhance the voxels whose properties

are similar to a VRS. The formulation of these ﬁl-

ters are based on the mean and standard deviation of

the CSF, GM and WM, which requires the segmen-

tation of the brain in those regions. Departing from

the original proposal (Descombes et al., 2004), we

have used the segmentation method proposed in the

previous section. The original proposal was based on

a region growing method; the segmentation provided

by this method has potential to be impaired by a class

leaking into another region, which may require inter-

vention from the user, while the segmentation method

proposed in the pipeline is completely automatic.

The candidates obtained as output of the com-

bined response of the ﬁlters have to be aggregated

as a possible VRS. Departing from the authors, for

each positive value, we have found the direction for

which that response happened, and we followed it by

advancing one slice in each iteration until there isn’t

more positive values in the 9 voxels right in front of

the last that have been included; doing so, it was pos-

sible to ﬁnd a starting point and the length of each

candidate.

Having the candidates to dilated VRS, their geom-

etry, interaction among them and their intensity were

modeled with a Marked Point Process (Descombes

et al., 2004). Each candidate, s, has a point, corre-

sponding to the point where it starts, x, and a mark, l,

AFullyAutomaticToolforCountingVirchow-RobinSpacesinMagneticResonanceImagingforLacunarStrokeStudy

that is a 3D vector representing the length and orien-

tation of the candidate in each axis; so, s = (x, l).

Given a conﬁguration of marked points (c), the

model, h, is composed by a prior term, f , and a data

term g (Descombes et al., 2004),

h(c) = f (c)g(c) (1)

The prior term models the length of the candidates

and relations between them, while the data term takes

into account the intensity along the candidate, as well

as the intensity after each of its ending voxels.

In order to select the conﬁguration of candidates

corresponding to real dilated VRS, Descombes em-

ployed a Reversible Jump Markov Chain Monte Carlo

algorithm with simulation annealing to globally op-

timize the model. Diverging from Descombes, who

have used a geometrical cooling schedule, we have

chosen to follow an exponential cooling schedule

(equation (2)), as described in (Murphy, 2012), be-

cause we have found that it provided better conver-

gence.

= T

, (2)

where T

is the temperature at iteration t, T

is the ini-

tial temperature, deﬁned as T

= 1, and C is the cool-

ing rate, deﬁned as C = 0.8.

2.4 Visualization Application

In order to facilitate the observation and analysis of

the detected dilated VRS, we developed a graphical

application as presented in Figure 4. Common to all

three panels is a section that allows the user to select

the desired panel (Figure 4(a)-I). The main panel (Fig-

ure 4(a)) has a section to select the image to be loaded

and the image with detected VRS. There, it is, also,

possible to turn on or off the layer with the detected

dilated VRS and to check the counting of the detected

structures (II). In III, we present the 3D reconstruc-

tion of the selected dilated VRS in the viewers (IV)

with the image of the ROI that has the detected VRS

overlaid.

The application permits to visualize the 3D recon-

struction of the ROI, as well as the detected VRS, as

depicted in Figure 4(b) for the basal ganglia. Finally,

the ROI with the detected VRS overlaid is, also, re-

constructed (Figure 4(c)).

3 RESULTS AND DISCUSSION

The database used for the detection of dilated VRS

was acquired at the Hospital of Braga, Portugal, from

9 elderly patients, whose mean age was 69.4, that had

a clinical minor stroke within 6 months of the cere-

bral MRI acquisition and had at least one vascular

risk factor as hypertension, diabetes or high choles-

terol levels. For each of them there was available

a 3D T1-weighted MPRAGE sequence (176 sagit-

tal slices, matrix 256 X 256, 1 mm isotropic reso-

lution, TR 2730 ms, TE 3.48 ms), acquired with a

Siemens Avanto 1.5 T scanner. To segment the tissues

of our MPRAGE sequence images, we used the Ran-

dom Forest that was trained to participate in Grand

Challenge on MR Brain Image Segmentation (MR-

BrainS

). Although the intensity range is different,

the normalization procedure permitted to normalize

the intensities between both databases.

3.1 Tool

Figure 4 depicts the potential of the proposed tool.

With it, physicians can easily retrieve the number

of dilated VRS for each ROI, as well as inspect the

scan of the subject to conﬁrm if it presents abnormal-

ities related to small vessel diseases, or others, which

would allow a better understanding of the correlation

between dilated VRS and those abnormalities. 3D re-

construction of the detected dilated VRS may be use-

ful to provide insight into the spatial distribution of

those structures, as well as if there exists any correla-

tion between the distribution and the diseases, while

3D reconstruction of the ROI permits to conﬁrm if

it was well extracted. The 3D reconstruction of the

ROI, or brain, with VRS overlaid is useful to under-

stand if its propagation through the brain parenchyma

coincide with the perforating arteries.

When the FLAIR sequence is available, the

method to segment tissues is able to segment WM

lesions. This potentiality makes the tool capable to

measure its number and volume, which may be use-

ful for studies that hypothesize a correlation between

dilated VRS and WM lesions.

3.2 Detection of Dilated VRS

The ﬁrst row of Figure 5(a) and Figure 5(b) show

some examples of dilated VRS detected by the al-

gorithm in the basal ganglia and cerebral WM, re-

spectively. In both cases, the examples seem to have

properties of dilated VRS, such as the low intensity,

surrounded by normal tissue, and small size. In the

second row of Figure 5(a) and Figure 5(b) is shown

the 3D reconstruction of each detected VRS, which

allows the physicians to observe the tortuosity; the

green mark is located in the voxel that was selected

http://mrbrains13.isi.uu.n

BIOIMAGING2015-InternationalConferenceonBioimaging

(a)

(b)

(c)

Figure 4: Application for the visualization of the detected VRS. In a) the image of the ROI is depicted with the detected dilated

VRS and its counting, b) shows the 3D reconstruction of the ROI and the detected VRS; while in c) the 3D reconstruction of

the VRS overlaid over the ROI is shown.

AFullyAutomaticToolforCountingVirchow-RobinSpacesinMagneticResonanceImagingforLacunarStrokeStudy

(a)

(b)

Figure 5: Examples of detected dilated VRS and their 3D

reconstruction, in a) basal ganglia and b) white matter.

in the viewer, facilitating the correspondence between

the reconstruction and the image by the user. In the

ﬁrst example of Figure 5(a) the green mark is over the

ﬁrst voxel of the VRS, while in the last of Figure 5(b)

is over the last voxel, but in both cases it is possible

to observe that the dilated VRS changes its direction.

4 CONCLUSIONS

In this paper, we have proposed a fully automatic tool

to facilitate the analysis and studies on the relevance

of dilated VRS as a biomarker for lacunar strokes and

its association with dementia. It comprises a set of

preprocessing procedures that improves the quality of

the image for the problem we tackled. We have pro-

posed alternative steps in an existing algorithm to de-

tect the VRS as well as an in-house tissue segmenta-

tion algorithm. These modiﬁcations improve robust-

ness of the detection of VRS in the presence of le-

sions and tissues atrophy. We believe this improve-

ment in the robustness is required for a fully auto-

matic pipeline.

A relevant feature of our tool is the possibility to

extract a ROI, and to count dilated VRS only there,

which is impractical in a large scale study using hu-

man experts, as is our goal. The visualization of the

detected VRS facilitates its analysis, being useful to

help the physician decide if a lesion is a lacunar stroke

or a VRS, when there are doubts. A tool with this fea-

tures may facilitate the standardization among studies

on dilated VRS as a global marker of cerebral vascu-

lopathy and WM lesions.

ACKNOWLEDGEMENTS

We thank Joana Festa for her collaboration in the de-

velopment of the visualization application.

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AFullyAutomaticToolforCountingVirchow-RobinSpacesinMagneticResonanceImagingforLacunarStrokeStudy