1. (Semi)Automatic Quantification of the Internal Elastic Lamina Fenestrae in Remodeling Arteries A Feasibility Study Master Thesis Harald Groen

2. Outline Introduction Problem Definition Vessel Wall Composition Vessel Wall Remodeling Materials and Methods Image Analysis Summary Future Work

3. Introduction

4. 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

5. Vessel Wall Composition Cross-section electron micrographs of a mesenteric artery (mouse) bar = 10 μm (left), 1 μm (right) Dora et al. 2003

6. 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

7. 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 arteries Control, pre- (day 17) and postpartum (7 days)

8. Remco Megens Materials and Methods Uterine artery: ± 2 x 0.3 mm 7.5 x 3.5 x 1.0 cm, 10 ml

9. 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

10. 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

11. 3D Stack Example 103x103x32µm Elastin (Eosin)Nuclei (Syto13) Adventitia ↓ Lumen

12. 3D Stack Example: Elastin 103x103x32µm Elastin (Eosin) Adventitia ↓ Lumen

13. Image Analysis

14. Model Vessel: 2D Imaged part

15. Uterine Artery: 3D Internal radius ≈ 118 µm Consistent with literature (depth)

16. Real Uterine Vessel: Unfolded 103x125x24µm Adventitia ↓ Central line

17. Tissue Layer Manual Selection Average elastin intensity (red) as function of r

18. Spatial Maximum Laplacian Test image Spatial Maximum Laplacian Threshold Potential Fenestrae

19. Quantification and Selection Compared with manual selection: False Positives: 40% Missed: 20% Quantification Fenestrae: - Density (mm -2 ) - Mean area (µm 2 ) - Relative area (%) Artery: - Vessel diameter (µm)

20. Results

21. 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

22. 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?

24. Questions / Remarks