AUTOMATIC DETECTION OF IN VITRO CAPILLARY TUBE

NETWORK IN A MATRIGEL ANALYSIS

Eric Brassart

, Cyril Drocourt

, Jacques Rochette

, Michel Slama

and Carole Amant

LTI, Univerty of Picardie Jules Verne and IUT Amiens, France

DMAG, EA 3901, Amiens, France;Univerty of Picardie Jules Verne, Amiens, France and CHU

INSERM, ERI-12, Amiens, France;Univerty of Picardie Jules Verne, Amiens, France and CHU

Keywords: Angiogenesis, Image analysis, Capillary tube network.

Abstract: Angiogenesis, the formation of new capillary

blood vessels from pre-existing vessel, has become an

important area of scientific research. Numerous in vivo and in vitro angiogenesis assays have been

developed in order to test molecules designed to cure deregulated angiogenesis. But unlike most animal

models, most in vitro angiogenesis models are not yet automatically analysed and conclusion and data

quantification depend on the observer’s analysis. In our study, we will develop a new automatic in vitro

matrigel angiogenesis analysis allowing tube length and the number of tubes per cell islets as well as cell

islet and tubule mapping to be determined, percentage of vascularisation area, the determination of ratio of

tubule length per number of cells in cell islet and, ratio length/width per tubule determination. This new

method will also take image noise into account. Our method uses classical imaging quantification. For the

first image processing we used image segmentation (Sobel type edge detection) and artefact erasing

(morphologic operator). Subsequent image processing used Snakes: Active contour models in order to

precisely detect cells or cell islets. We suggest that this new automated image analysis method for

quantification of in vitro angiogenesis will give the researcher vascular specific quantified data that will

help in the comparison of samples.

1 INTRODUCTION

Angiogenesis, a complex process whereby new

blood vessels form from pre-existing vasculature in

response to proangiogenic factors, is an essential

physiological process required for growth and

development (Folkman J. 1971 and 1992).

Angiogenesis represents the unique process by

which evolution tissue may be supplied in essential

elements provided by blood. Angiogenesis is

therefore involved in major physiological processes

including embryonic development, female

reproduction, wound healing and collateral

generation in the myocardium. Dysregulated

angiogenesis plays a critical role in various

pathological mechanisms such as solid tumour

formation, metastasis, childhood haemangioma,

diabetic retinopathy, macular degeneration, psoriasis

and in inflammation-related diseases such as

rheumatoid arthritis, osteoarthritis and ulcerative

colitis.

2 PRIOR AND RELATED WORK

In this way, drug design in order to cure

dysregulated angiogenesis is evident. Many in vivo

and in vitro angiogenesis model have been

described. But unlike most animal models in which

blood flow doppler analysis allows vascularisation

quantification (Couffinhal T, 1999), most in vitro

angiogenesis models are not yet automatically

analysed and conclusion and quantification depend

on observer analysis (Vincent L., 2003). most in

vitro angiogenesis cannot be automatically

quantified and require observer participation. The

determination of the effect of drugs on vasculature

development requires the comparison of samples

and the use of data analysis standardization. In this

484

Brassart E., Drocourt C., Rochette J., Slama M. and Amant C. (2008).

AUTOMATIC DETECTION OF IN VITRO CAPILLARY TUBE NETWORK IN A MATRIGEL ANALYSIS.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 484-489

 SciTePress

study, we focused on an in vitro endothelial cell

differentiation matrigel assay automated image

analysis methods for the quantification of

angiogenesis. Most of the time, tube length and the

number of tubes per cell islet are the only data in

publication that can be found, and are quantified by

the observer himself. Few publications have

described an automatic image analysis approach.

One of these publications, (Niemisto A., 2005)

describes an automatic image analysis method for

quantification of in vitro matrigel angiogenesis. But

in our study we will develop a new automatic in

vitro matrigel angiogenesis analysis allowing in

addition cell islet and tubule mapping, percentage of

vascularisation area determination, ratio of tubule

length per number of cell in cell islet determination,

and ratio length/width per tubule determination. In

this study we will develop a new method in order to

take image noise into account (particles, air bubbles

included in the matrigel).

3 IMAGE ANALYSIS

3.1 Introduction

According to Nicolas Ayache, the problems

encountered in the analysis of medical imagery can

be separated into several categories:

− Restoration: this step consists of recreating an

improved image, in which several faults

connected with the physical acquisition process

have been eliminated (noise reduction, ...).

− Segmentation: separation consists of extracting

points, lines or regions which are then used as

data in complimentary work such as

realignment, measurement, analysis of

movement, visualisation etc.

− Realignment: this a problem common to many

tasks concerning the analysis of medical

imagery, and is necessary to compare the

images acquires from one single patient, or with

varying modalities.

− Morphometry: this consists of studying the

geometry of the forms, in particular the

calculation of average forms and the variations

around theses forms.

These treatments occur at different time and in

different order. The reference image we were using

in this article is in figure 1.

Figure 1: The reference image, to which all the developed

processing will be applied in this article.

3.2 Segmentation

After having tested several methods of binarisation

(Fisher, Otsu,) (Antti Niemistö 2005) we determined

that this type of simple processing was not suitable,

principally because of its sensitivity to the variation

in luminosity within the image. Indeed, projections

of light on to tissues are not homogenous, and often

darker zones appear at the edges of the images

acquired, leading to poor separation of classes in

OTSU's formulation. We therefore chose to pre-

process our images in several successive stages,

allowing us to isolate only the cells and the

background. These steps, undertaken one

independently of the other correspond to traditional

processing in digital imagery, but bring about an

efficient solution:

− a detection of the contours by means of the use

of a gradient operator(

∇

), and more

specifically the norm of this operator.

)y,x(I

⎟

⎠

⎞

⎜

⎝

⎛

∂

⎟

⎠

⎞

⎜

⎝

⎛

∂

=∇

with its

discreet estimation coming down to the calculation

of two convolutions in the x and y directions. The

operator we preferred is that of Sobel (Sobel, I

1973); see Figure 2.

Figure 2 : Detection of the contours with Sobel's operator.

AUTOMATIC DETECTION OF IN VITRO CAPILLARY TUBE NETWORK IN A MATRIGEL ANALYSIS

485

− Closure of the objects in the image which

allows the joining of neighbouring pixels to

close the contours and the unconnected

surfaces. This allows us to make the "textured"

surfaces homogenised and to create a complete,

uniform object. (Figure 3),

Figure 3 : Convolution and closure of the image

− Elimination of objects which are too small

(Restoration phase). The aim here is to eradicate

objects whose size does not satisfy the criterion

of the average size of all the images composing

the image. In the majority of the images

contained in our library, this step permits us to

attribute a sufficiently precise localisation of the

network, without necessarily being able to

identify the cells (or mass of cells) of the

connecting tubes.

Figure 4 : Isolated network after noises elimination and

the isolated elements.

The second phase of this study consists of extracting

the different elements characteristic of what we will

call the cells (or the mass of cells) from the image,

and the tubes joining the cells, when they exist. The

idea developed in this paper is firstly to isolate

everyone which resembles cells, and then to try,

from these latter, to establish the connections (tubes)

which, after all, characterise the mesh of our

network. The different stages put into place are the

elimination of the various noises in the image,

(reflection from bubbles of air in the network,

particles, non-consideration of isolated cells; the

elimination of tubes. From the resulting image, with

the remaining lines, we are specifically looking for

the exact contour of the cells or mass of cells. To do

this, we used an algorithm based on the active

contours, for which the initialisation of the starting

points is done automatically.

Erosion of the picture descended of the previous

stage permits to suppress the information of type

tubes and to only keep information of type cells.

This stage remains the most appreciable part of our

algorithm because it is from this one that the set of

cells will be initialized. (Figure 5),

Figure 5 : Erosion of the image and initialisation of the

starting point characterising the cellular mass.

− Use of snakes

A snake (Kass M 93, Xu C 97) is an elasticised curve

which can be modelled by a parametric shape

normalised as follows:

→

v ( s ) = {x (s), y(s)}

Where s is the curvilinear abscissa or the parameter

on the curve ∈ in the spatial domain Ω,

It ensues from the previous definition that a model

of snake is a problem of optimisation of a functional.

− Several resolution approaches exist, let us quote

a variation method which consists of resolving

Euler's

Ω = [0, 1] → R

v(s) is the vector of position of the point of contour

of coordinates x (s) and y(s),

v(1) and v(0) are the vectors of position of the

extremities of the contour.

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

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The total energy of the contour which we try to

minimize is represented by the following function

(Kass M 93):

ake

∫

snake

(v(s)) ds =

∫

int

(v(s)) + E

image

(v(s)) + E

cont

(v(s)) ds

Where E

int

represents the internal energy of the

snake, E

image

is the energy derived of the image

(contours, gradients) and E

cont

represents the energy

of constraints.

Initialisation of the detection process.

One of the major concerns which exist within the

framework of the use of the active contours is the

initialization. Indeed, in the majority of the

applications using this technique, the initialization of

snakes is done manually by asking the user to select

points around the shape to detect what will

constitute the initial contour. In our application each

image zone corresponding to a cell is automatically

framed by the max coordinates resulting from a

labelling procedure The initial points correspond to

the totality of points characterising the perimeter of

each rectangle concerned (figure 6). The number of

iteration points on the snakes is limited to 200, not to

have a too long treatment on images. Of course, if

the snake converges toward a solution before this

maximum number the process stops on the usual

criteria.

Figure 6 : Initialisation of the snake son the zones marked

in white.

The use of the snakes permits to isolate precisely the

surfaces associated with the cells (Figure 6). Finally

the subtraction of the image obtained with that of the

previous step to isolate the tubes (Figure 7).

Figure 7: Initialisation of the snakes on the zones marked

in white.

Figure 8: Detection of the tubes.

By carrying out a subtraction between the binarised

images of the cells and those of the tubes we are

now able to establish the network of cells, i.e. the

cells and the tubes that connect them. To represent

this network, we positioned the centre of theses cells

by calculating the barycentre of each one of them.

The result of this method is detailed in the following

part of this article.

4 RESULTS

We have developed a new software for the

processing of images, able to automatically analyse

angiogenesis images. For this article a collection of

10 images of this type was used to validate the

results obtained. This software was written under

Matlab with the image processing toolbox. All the

images were obtained using a light microscopy. The

concern in these images acquirement is to obtain

images having sufficient contrast to be able to

clearly show the cells and the tubes, and the noise

inherent to these images as at the lowest level as

possible :

• homogenous light to avoid the effects of

poor binarisations,

AUTOMATIC DETECTION OF IN VITRO CAPILLARY TUBE NETWORK IN A MATRIGEL ANALYSIS

487

• particles and bubbles of air leading to

the detection of objects capable of being

assimilated with cells.

We will show in this part the results obtained with

various processing on a reference image, but the

reader will find the complete results obtained from

all the samples used at the following address:

www.iut-amiens.fr/Angio-results.

The first result given is to familiarize the practitioner

with the vascularisation surface of the sample that

has been imaged. On the image in figure 8, the

percentage of vascularisation (%Sv) obtained is

10,247%. The values given are determined by the

following ratio of surfaces :

The surface of the pixels within the contours (Sc)

divided by the total pixel surface of the image (St).

Figure 10: percentage of the vascularisation surface: %Sv

= Sc/St.

The results obtained are given in the form of a ratio

that the user of our program may consult following

the processing. On one hand it is visual with the

illustration in figure 9, on the other hand it is

numerical by means of consultation of the statistics

shown in the following table.

Figure 11: Aspect of the network corresponding to the

cellular development.

Report of the detection:

Connection

1 connected with 3

2 connected with 6

3 connected with 6

6 connected with 10

8 connected with 11

11 connected with 12

Number of Cells: 14 :

Number of Tubes: 48

Number of Connections : 6

Mean tube length: 96,923 (in arbitrary units)

Surface of the cells: 4088 (in arbitrary units)

5 CONCLUSIONS

We have developed a new technique of automatic

detection of a vascular network in a matrigel gel.

This technique is based on basic image processing

techniques such as the detection of contours,

morphologic operators combined with more

sophisticated processing such as the use of active

contours. On this latter point we have developed the

original idea of automatic placement of the initial

points on the cells. In literature dealing with this

aspect, there are very few methods avoiding the

placement of these points manually. Our technique

can be used to measure the length and size of tubular

complexes automatically, to localize cell islet and

tubule, to measure the percentage of the

vascularisation area, the ratio of tubule length per

number of cells in a cell islet and the ratio

length/width per tubule. Our software also propose

the structure of the capillary network.

Concerning the software, a certain number of

developments still have to be completed. Indeed,

during the detection of the cells in the image, a

certain number of them are considered as noise or

are ignored since the contrasts are not significant to

allow automatic detection (the light is not adapted).

In order to consider them as an integral part of the

mesh, the practitioner must be able to make them

active by manual intervention, and reintroduce them

in the detection of the capillary network.

6 PERSPECTIVES

This software will help the researcher to quantify

samples and to determine the effect of new anti-

angiogenic or pro-angiogenic agents in deregulated

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

488

angiogenesis processing. An other application of

these new in vitro angiogenesis quantification

techniques in ischemic hind limb or ischemic

myocardial cell therapy will be to test the ability of

bone marrow stem cells or endothelial progenitor

cells to differentiate in endothelial cells and to

establish a vasculature shortly before the injection in

the ischemic tissue.

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