(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