tools for shape analysis of vascular response using two photon laser scanning microscopy by han van...
Post on 21-Dec-2015
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Tools for Shape Analysis of Vascular Response using
Two Photon Laser Scanning Microscopy
ByHan van Triest
Committee:
Prof. Dr. Ir. B.M. ter Haar RomenyDr. M. A. M. J. van ZandvoortDr. Ir. H. C. van AssenA. Vilanova i Bartrolí, PhDR.T.A. Megens, MSc
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Overview
1. Biological Introduction2. Technical Introduction3. Vessel Radius Estimation4. Cell counting5. Conclusion and Recommendations
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Biological Introduction
Vascular diseases are a big problem in the western world.
It is estimated that arteriosclerosis is the underlying cause of 50 % of all deaths in the western world
To unravel the underlying mechanisms more research is required
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Biological Introduction – Vessel Anatomy
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Biological Introduction – Remodelling
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1. Excitation2. Energy loss processes3. Emission
Energy of Photon:
hhE
Technical Introduction – Fluorescence
1
2
3
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Technical Introduction – Confocal Laser Scanning Microscopy
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Technical Introduction – Confocal Laser Scanning Microscopy
Advantages:• Optical sectioning
Disadvantages:• Excitation of out-of-focus regions• High energy of excitation photons• Low penetration depth
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Technical Introduction – Two Photon Laser Scanning Microscopy
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Technical Introduction – Two Photon Laser Scanning Microscopy
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Technical Introduction – Two Photon Laser Scanning Microscopy
Advantages:• No pinhole to block out-of-focus light required• Increased penetration depth• Excitation photons of lower energy• Imaging of viable tissue• Multiple dyes usable for targeting of different structures
Disadvantage:• Higher wavelength limits maximal achievable resolution
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Technical Introduction – Imaging
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Processing – Description of Vessels
Features:• Radius of the vessel• Ratio vessel wall thickness – vessel radius• Cell volume fraction
Needed:• Vessel Radius• Number of Cells
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Radius Estimation
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Radius Estimation – Methods
1. Statistical methods: Least squares estimators2. Robust statistics: Reduction of the influence of outliers3. Hough Transform
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Radius Estimation – Hough Transform
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bxay
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Line through a point in image space
Set of parameters that describe the point
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Radius Estimation – Hough Transform
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Radius Estimation – Circular Hough Transform
Circle can be described by: 222 )()( ryyxx cc
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Radius Estimation – Hough Transform
Advantages:• Robust against noise• Able to find partly occluded objects
Disadvantages:• Expensive, both computational and memory cost
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Radius Estimation – Proposed method
A circle is defined by three non co-linear points.
• Store only center coordinates• Weight vote by average distance
between p1, p2 and p3
Find r by voting for most likely value of the radius
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Radius Estimation – Finding Edge Points
A global threshold is infeasible due to differences in optical paths for emitted photons
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Radius Estimation – Finding Edge Points
Modified Full Width at Half Maximum:
maxI InsideOutside
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Radius Estimation – Experiments
• 20 images, 10 single slices, 10 taken from three dimensional stacks
• Test images have both sides of the wall vissible• Groundtruth given by the average estimate of 12
volunteers• Results compared with common least squares
estimator• Tests are performed for values of α between 0.2 and
0.8 in steps of 0.05, and using 20 to 250 points in steps of 10
• In total 24960 estimates are made
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Radius Estimation – Influence of α
xz-scan:
z-stack slice:
Blue line: LSE
Red line: MHT
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Radius Estimation – Influence of number of points
xz-scan:
z-stack slice:
Blue line: LSE
Red line: MHT
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Radius Estimation – Conclusion
• Proposed method outperforms least squares fitting method for xz-scans
• Proposed method performs equally compared to least squares fitting method for z-stack slices
• The best value for α used in the proposed method is α = 0.4
• At least 100 points is required for a stable result using the proposed method
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Cell counting
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Cell Counting – Algorithm
Noise Reduction
Potential Center Extraction
Potential Edgepoint Extraction
Edgepoint Selection
Ellipsoid Fitting
Oversegmentation Reduction
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Cell Counting – Noise Reduction
Edge-preserving filtering: Median
Filtering
Each pixel is replaced by the median of its surrounding
Purple line: Original objectBlue line: Degraded objectRed line: Median filter, kernel
width 5 pixelsBlack line: Median filter, kernel
width 25 pixels
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Cell Counting – Potential Center Detection
Assumption: Blob-like structures
Center is maximum of the blob
Local maxima within a region are potential centers.
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Cell Counting – Potential Edgepoint Extraction
• Sample rays from each potential center• Rays intersect points along a generalized spiral
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Cell Counting – Potential Edgepoint Extraction
Constraint:Points on a downward flank
These points can be found at points in which the second order derivative switches from negative to positive.
Blue line: Image intensity along ray
Purple line: First order derivative
Sienna line: Second order derivative
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Cell Counting – Dynamic Programming
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Shortest Route: AbcB
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Cell Counting – Edgepoint Selection
Find set of most likely edge points
Cost function: 2
max
2max
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Cell Counting – Ellipsoid Fitting
0222 dzryqxpyxhzxgzyfzcybxaQ
Ellipsoid can be described by a quadric, a general polynomial in three dimensions of order two:
Axes proportions
Orientation Position Size
Fitted on the data using a least squares fitting procedure
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Cell Counting – Oversegmentation Reduction
1. Find overlapping nuclei2. Check wether nuclei are parallel3. Merge the sets of edgepoints of parallel overlapping nuclei4. Perform Ellipsoid fitting on the combined data sets
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Cell Counting – Results Before Merging
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Cell Counting – Results After Merging
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Cell Counting – Discussion
Three types of frequent mistakes:A Incorrect merging of two blunt nucleiB Center of cell not foundC No distinct directions
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Cell Counting – Discussion
A another problem is due to leakage of light from other colors
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Cell Counting – Conclusion
Although the method only has been tested on a single dataset, the results show to be promising.
Most of the cells are found while there is a relatively small amount of false negatives and false positives
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Recommendations
• Test the algorithm on more datasets• Investigate the influence of parameters• For the calculation of the cost during the dynamic programming
step, take into account more points on the surface• Remove outliers in the selected set, as outliers have great effect
on the least squares algorithm• Optimize the imaging parameters to get as litle non cellular
structures as possible • Classify the cells into subclasses
Questions?