VASCULAR NETWORK SEMI-AUTOMATIC SEGMENTATION

Using Computed Tomography Angiography

Petr Maule, Jiří Polívka and Jana Klečková

Department of Informatics, University of West Bohemia, Pilsen, Czech Republic

Keywords: Vascular Network Segmentation, Computed Tomography Segmentation, Portal Vein, Mesh Exporting.

Abstract: The article describes simple and straightforward method for vascular network segmentation of computed

tomography examinations. Proposed method is shown step by step with illustrations on liver's portal vein

segmentation. There is also described method of creating and exporting mesh and simple way of its

visualization which is possible also from a web-browser. The method was developed to provide satisfactory

results in a short time and is supposed to be used as geometry input for mathematical models.

1 INTRODUCTION

Medical diagnostic methods are quickly evolving

branch of research. Properties of current medical

instruments are improved year by year and larger

amount of data is being stored. Important part of

medical diganostic methods is to correctly process

imaging data which comes from different modalities.

A lot of mathematical models have been developed

to simulate functionality of different organs. Such

models require inputs where some of them (like

geometry) can be provided by processing computed

tomography (CT) images. This article describes our

experience with vascular tree segmentation process

in order to gain geometry information for liver's

model which is under development.

Geometry detection has been already solved but

finding any suitable non-commercial software is not

so easy. Therefore we are presenting here simple and

straightforward method for geometry detection

which can be implemented in a short time.

2 METHOD DESCRIPTION

We propose universal procedure for geometry

information detection. This procedure is based on

presumption that we know range of densities of the

desired object. In order to liver's vascular network

we want to find geometry of portal vein. We will

describe whole process on a computed tomography

dataset.

2.1 Input Examination

Input examination used in this article is CT

angiography examination stored in DICOM format

consisting of 1256 slices of 0.6 mm slice thickness.

The examination covers bottom body part starting in

a half of livers and ending before knees.

Segmentation process of the liver's vascular network

should be able to work also with non-complete data

sources like this. At this point we cannot expect full

network, but only the part which is covered by the

examination.

2.2 Desired Outcome

We need to find surface model of vascular network.

It means that we must find just surface of the

network and describe it as coordinations of vertexes

and list of connections between them forming

triangles or rectangles lying on surface. The ideal

surface should be formed by a mesh describing

smooth tubes (cylinders) of diameters corresponding

to detected vessels. But it is a task for a future work

at the moment.

2.3 Preparation

Input examinations often contain more data then it is

required and it makes process of vessels

segmentation more time consuming. By selecting a

sub-volume we significantly reduce time required

for processing. Sub-volume selection can be

described as finding upper left and lower right x, y, z

coordinations of the sub-volume (see Figure 1).

323

Maule P., Polívka J. and Kle

cková J. (2010).

VASCULAR NETWORK SEMI-AUTOMATIC SEGMENTATION - Using Computed Tomography Angiography.

In Proceedings of the 5th International Conference on Software and Data Technologies, pages 323-326

DOI: 10.5220/0002917203230326

 SciTePress

Figure 1: Sub-volume selection.

2.4 Segmentation

Segmentation is based on assumption that we know

range of densities which desired object should have.

In our case range of portal vein densities is 100-150

HU. Input examinations can contain noise which

makes segmentation more difficult. We use

averaging method of near neighborhood where

dimensions of the considered neighborhood we are

of the smallest object which we want to detect. In

our case we want to find all vascular structures at

least with diameter of 2 mm. We use neighborhood

of spherical area with diameter 2 mm. We compute

average density for each pixel of the sub-volume

resulting in intermediate image where only two

values are present (1 for average density of desired

object, 0 elsewhere).

Portal vein comes from larger dimensions to

smaller. Trunk of the vein is significant and can

measure about several centimeters in diameter. As

the vascular tree divides in branches the diameter is

less and less. The smallest parts are out of

instruments possibilities and are not visible in scans.

Figure 2 shows intermediate image. It requires

further processing steps.

2.5 Grouping Method

Figure 2 shows that basic segmentation process

produces too much points. Idea of further processing

is to divide all points into groups. Each group

contains only points which border with each other.

For this purpose we use seed filling algorithm. For

each point of intermediate image with value of 1 we

search his neighborhood in all 26 directions. If there

is a point with value of 1 too it will be added to the

same group as initial point (seed filling). The points

which have been already assigned to a group will be

skipped. We mark groups by numbers increasing by

1. When all groups are found we can count how

many of the points each group have and easily we

can remove groups where number of points does not

exceed certain level. Threshold number is

application depended. In our case we will finally

leave just one group which will correspond to the

portal vein which is the group with the highest

number of the points. But in first tries it is better find

a threshold value which will provide removing of

those points which persisted by noise or by limited

sensitivity of the instruments. Figure 3 shows our

case using threshold of 50 000 points.

2.6 Final Adjustment

Result shown in Figure 3 contains desired portal

vein but it contains also other connected parts which

should be removed. We recommend to cut these

parts already during creation of intermediate image.

It is required to find bounding box of these parts in

input examination.

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Figure 2: Intermediate image.

It is similar to finding sub-volume described in

section 2.3. It means that for each part we must find

upper left and lower right coordinations (x, y, z)

forming a box which will not be present for further

processing. Skipping these areas from intermediate

image processing will influence grouping method

(section 2.5). Correct bounding box of the object to

be removed should break connections between

object to be removed and desired object. It should

not cut any part of the desired object (portal vein).

This process will lead to division of groups where

objects to be removed will have their own groups

with less points thus it will be removed according to

used threshold value. We used four bounding boxes

to remove unwanted objects. The final result is

shown in Figure 4.

Figure 3: Grouping method result.

Figure 4: Portal vein network.

3 MESH GENERATION

Our method described in section 2 results in three-

dimensional array where all places of vascular

network are marked with value of 1. Mesh can be

described as a list of vertex coordinations and

description of faceset stored as indexes of the

vertexes composing each face.

Mesh generation process is a process where only

surfaces of the vascular network should be stored.

We store mesh by processing all elements of the

array. At those places where we detect transition

from 1 to 0 we store it as a mesh face. In more

details all elements with value of 1 undergo testing

whether any of their neighbouring elements in 6

directions (left, right, up, down, forward, backward)

contains value of 0 and if yes we store face

information. As far as we use DICOM format

examinations we know dimensions of each element

of the 3D array so we use it for computing

coordinations of vertexes. We store face in a

rectangle form therore a face will be stored as 4 new

vertexes and one description of the face as 4 vertex

indexes. Vertexes of adjacent faces can be shared so

we prevent duplicities by testing whether required

vertex has been already stored.

3.1 File Formats

Important part of vascular network segmentation is

visualization. Visualization offers important

feedback. Good visualization can help you with

localization of parts to remove and tells where to

remove it (see section 2.6). Visualization software

exists but it differs in licence conditions and

supported file formats. We use mesh exporting to

Virtual Reality Modeling Language (VRML) file

format because VRML files is easy to create and

there can be also downloaded plugins into a web

browser and you can display it directly in the web

browser. For more information about VRML see [2].

4 FUTURE PLANS

Our goal is to improve this tool and make it usable

from experimental database medical system [1]. The

database stores examinations of different kinds

including CT examinations. We hope that we will

develop a system which will be able to segment

imaging examinations starting with CT examinations

according to needs of future mathematical models

which will run above the experimental database

VASCULAR NETWORK SEMI-AUTOMATIC SEGMENTATION - Using Computed Tomography Angiography

325

system. This will require to develop description

system of mathematical model needs. All segmented

vascular network structures will be stored in the

database and will be available by web-based

browsing system in form of VRML files.

5 CONCLUSIONS

We proposed simple straightforward method which

is able to segment and export vascular network in a

mesh form. This method is shown on example of

liver's vascular network segmentation. We have

described method which can be easily used for

segmentation of other vascular networks.

Description of each step with graphical figures can

help others who are dealing with this problematic.

Described method can be implemented in a short

time and gives satisfactory results which can be used

for further processing or base for mathematical

modeling.

ACKNOWLEDGEMENTS

The work presented in this paper is supported by

The Czech Science Foundation project 106/09/0770

dealing with brain perfusion modelling.

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