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Master Thesis Harald Groen

(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

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Master Thesis Harald Groen

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  1. (Semi)Automatic Quantification of the Internal Elastic Lamina Fenestraein Remodeling ArteriesA 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

  6. Vessel Wall Remodeling 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 Hilgers et al. 2004

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

  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 Elastin (Eosin) Nuclei (Syto13) Adventitia ↓ Lumen 103x103x32µm

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

  13. Image Analysis

  14. Model Vessel: 2D Imaged part

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

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

  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 Threshold

  19. Quantification and Selection • Quantification Fenestrae: • Density (mm-2) • Mean area (µm2) • Relative area (%) • Artery: • Vessel diameter (µm) Compared with manual selection: False Positives: 40% Missed: 20%

  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?

  23. Questions / Remarks

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