Petr Flosman
Automatic processing
of biological images
Project leader: Ing. Petr Císař Ph.D.
CZ.1.07/1.1.14/01.0037
26.7.2012
Goal & Motivation
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Analysis of human embryo images
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Goal
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First 3 days after fertilization
Automatic detection of cell division during early embryo
phases
Find the beginning of division and first 3 mitosis
Motivation
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It could simplify the assisted reproduction
Automatic detection could help with choosing the most
“healthy” embryos for patient
Embryo & input data
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Cleavage – 3 or 4 mitosis in a row inside zona pellucida
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Image series
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Fast – no interphase
Embryo doesn’t change size
Several types
30 min. intervals
Chosen images
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Only with 2 or more mitosis
Not connected with the walls
Only human recognizable mitosis
22 image series
First methods
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Matlab
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Differential method
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Edge detection
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Difference between two images
Best results
Bad results – lot of noise
Histogram comparing method
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Bad results – changes of light in images
Improving differential method
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Cutting the dark background around the light plate
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Better results with differential method
Less noise
Automatic detection of embryo in this image
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Works very well - video
We didn’t use it for differential method - “shaking”
It can be used for detection of embryo movements and
for other detection methods
Cutting the background
Pixel
multiplication
A*B
A = original image
B = automatic
thresholding + finding
the largest white area
+ filling the holes
Detecting & cutting the embryo
Original image
A = thresholding + negative
A*B result
Filtration + filling
B = thresholding + filling
Embryo cut out
Differential method application
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Differential method on cut out background
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Good results
Filtration & Detection of mitosis
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Filtration of the graph
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Using “smooth” function
Detection of mitosis
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Using median
Detection examples
Human marked mitosis
(blue line)
Computer detected mitosis
(dotted area)
Detection mistakes examples
Computer detected one
mitosis as few
Problem with embryo movements
Results
True
False
True
False
Human recognition
Automatic detection
53
13
12
Main problem:
Significant embryo movements
Total number of
mitosis - real
Total number of computer
recognized mitosis
66
65
The success rate - recognizing
mitosis
80,30%
Error rate - unfound mitosis
19,70%
Error rate - recognizing mitosis
where they aren't
18,46%
Conclusion
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Detection has relatively good results
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Possible improvements
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With not moving or slightly moving embryos
With “normally” dividing embryos
Better detection and filtration of movements
Detecting number of cells in embryo
Practical use
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Reducing the strong light exposure during embryo
shooting
It can help the doctor to choose the best embryos for
embryo transfer
Practical use example
Sequence with no mitosis
Sequence with 4 mitosis
Thank you for your attention!