(Semi)Automatic Quantification of the Internal Elastic Lamina Fenestrae

in Remodeling Arteries

A Feasibility Study

Master Thesis

Harald Groen

Outline

• Introduction

• Problem Definition

• Vessel Wall Composition

• Vessel Wall Remodeling

• Materials and Methods

• Image Analysis

• Summary

• Future Work

IntroductionImage Analysis

BioMIM

Prof.dr.ir. B.M. ter Haar Romeny

QuestionPharmacology and Toxicology

Prof.dr J.G.R. de Mey

Molecular ImagingBioPhysics

Dr. M.A.M.J van Zandvoort

Problem Definition

A Feasibility Study:Investigate the change in fenestrae in

flow induced remodeling uterine arteries by using image analysis

Changes in: total number, density and area

Vessel Wall Composition

Cross-section electron micrographs of a mesenteric artery (mouse)

bar = 10 μm (left), 1 μm (right)

Dora et al. 2003

Vessel Wall Remodeling

Hilgers et al. 2004

Growth factor: EDHF → hyperpolarisation of SMCs

Remodeling involves changes in fenestrae

Hypothesis:Persistent increase in blood

flow increases the number and area of fenestrae in order to maintain the hyperpolarisation

Pregnancy Model

• During pregnancy, large increase in blood flow trough the uterine arteries: remodeling

• After pregnancy, decrease in blood flow: remodel back to original situation

• Pregnancy model, using uterine arteriesControl, pre- (day 17) and postpartum (7 days)

Remco Megens

Materials and Methods

Uterine artery: ± 2 x 0.3 mm

7.5 x 3.5 x 1.0 cm, 10 ml

Setup:TPLSM• Advantages:

– Deeper penetration in tissue– Fluorescence only from focal

point– Less bleaching

• Two photon has comparable results as confocal: Resolution 0.5 x 0.5 x 1.5 µm

• Optical sectioning without intervention

• Fluorescence technique• Labeling necessary

– Eosin: Elastin– Syto13 : Nuclei

Setup

• Two Photon Laser Scanning Microscopy– 60x magnification objective– NA 1.00– 2.0x optical zoom– 512 x 512 x ±170 voxels (≈ 100 x 100 x 45 µm)

• Image Analysis: Algorithms created in Mathematica

3D Stack Example

103x103x32µm

Elastin (Eosin) Nuclei (Syto13)

Adventitia

↓Lumen

3D Stack Example: Elastin

103x103x32µm

Elastin (Eosin)

Adventitia

↓Lumen

Image Analysis3D Image Unfolding

Tissue Layer Selection

Preprocessing

Detection

Spatial Maximum Laplacian Grayvalues

Segmentation PLUS

Quantification

Analysis

Number, Area, Density, etc.

V

Erosion and Dilation on Glued

Minima

Selection

Unfolding

Tissue Layer Selection

Spatial Maximum Laplacian

Grayvalues

PLUS

Quantification

Number, Area, Density

V

Erosion and Dilation on

Glued Minima

Selection

Model Vessel: 2D

Imaged part

Uterine Artery: 3DInternal radius ≈ 118 µm

Consistent with literature

Unfolding

Tissue Layer Selection

Spatial Maximum Laplacian

Grayvalues

PLUS

Quantification

Number, Area, Density

V

Erosion and Dilation on

Glued Minima

Selection

(depth)

Real Uterine Vessel: Unfolded

103x125x24µm

Adventitia↓

Central line

Unfolding

Tissue Layer Selection

Spatial Maximum Laplacian

Grayvalues

PLUS

Quantification

Number, Area, Density

V

Erosion and Dilation on

Glued Minima

Selection

Tissue Layer Manual SelectionUnfolding

Tissue Layer Selection

Spatial Maximum Laplacian

Grayvalues

PLUS

Quantification

Number, Area, Density

V

Erosion and Dilation on

Glued Minima

Selection

Average elastin intensity (red) as function of r

Spatial Maximum LaplacianUnfolding

Tissue Layer Selection

Spatial Maximum Laplacian

Grayvalues

PLUS

Quantification

Number, Area, Density

V

Erosion and Dilation on

Glued Minima

Selection

Test image Spatial Maximum Laplacian

Threshold ThresholdPotential Fenestrae

Quantification and SelectionUnfolding

Tissue Layer Selection

Spatial Maximum Laplacian

Grayvalues

PLUS

Quantification

Number, Area, Density

V

Erosion and Dilation on

Glued Minima

Selection

Compared with manual selection:

False Positives: 40%Missed: 20%

Quantification Fenestrae:- Density (mm-2)- Mean area (µm2)- Relative area (%)

Artery:- Vessel diameter (µm)

ResultsUnfolding

Tissue Layer Selection

Spatial Maximum Laplacian

Grayvalues

PLUS

Quantification

Number, Area, Density

V

Erosion and Dilation on

Glued Minima

Selection

Summary

• Unfolding is useful

• Detection and segmentation seems to work properly– Differences in semi-automatic and manual– No statistical significant differences between

groups: low number of samples and large variation in each group

– Results do not match with hypothesis and literature, but this is not due to the semi-automatically detection

Future Work

Molecular Imaging• More samples• Larger groups• Better filtering• More noise suppression• What is inside the

fenestrae?

Image Analysis• Better manual selection

for comparison• Minimizing user

involvement• Use more information

from the surrounding• Vesselness segmentation

for fenestrae detection?

Questions / Remarks