Transcript Understanding and Quantifying the Dancing Behavior of stem

Clinton Jung
[email protected]
Advisor: Bir Bhanu
Center for Research in Intelligent Systems
August 20, 2009
Overview
Background
 Project Objectives
 Technical Methods

 Otsu’s Method
 Connected Components
Algorithm
Results and Analysis
 Conclusions

Background
What are stem cells?
 Two lines

 Embryonic stem cells
 Adult stem cells

Five states




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Attached to substrate
Unattached
Dancing, or pre-attachment behavior
Death, or apoptosis
Mitosis
Background cont.

Stem cell culture conditions
 37 degrees Celsius
 Treated with cigarette smoke—traditional and
harm reduction
 Mouse embryonic stem cells are used because
they are easier to obtain and manipulate
 Matragel substrate

Video Capture
 Uses Biostation Hardware
 Time-lapse style
 3.5 hours, 106 still images
Background cont.

Stem cells alternate
between five states
 Dancing always comes
before a cell attaches
 Mitosis only occurs when
cells are unattached
 Apoptosis is often confused
with dancing behavior

Cell Arrangements
 Single Cell
 Colony
Project Objectives
 Crop images for the cells undergoing
dancing and play as video for each cell
 Develop image segmentation techniques
 Find connected components and compute
features
 Quantify the dancing phenomenon and
change in shape
Example of Video
Video Cropped for Dancing
Dancing Cells
Observations

Cells undergoing preattachment behavior
(dancing)
•
Often have several legs or
appendages when dancing.
These bulbs are roughly one
third or less of the original
size of the cell before dancing
A cell may undergo several
cycles of detachment,
dancing, attachment
May affect the state of
surrounding cells and
influence them in some
manner
Dancing often occurs after
mitosis but not necessarily
•
•
•

Cells attached to
substrate
•
Can be identified by an
increase in surface area and
exhibit a darker inner
intensity value
When cells attach, they lose
their circular shape and
instead become noncircular.
There were cells that were
semi-attached
•
Technical Methods

Image Segmentation- Process of dividing an image
into different segments (sets of pixels). This technique
can be used to locate objects and boundaries based on
lines, curves, contrasts

Benefits
 Automatically reduce image to simpler one to analyze
 Identify different components and features of an image

Simpler, processed images can then be
analyzed
Otsu’s Method


Step 1: Input Original Image and convert to grayscale
Step 2: Find threshold automatically using histogram. Two groups of
pixels created such that the intra-class variance is minimal and interclass variance maximal
Step 3: Split image into two classes (binary) based on the threshold
value. This final image will have pixel values of either 0 or 255.
Number of Pixels

Grayscale Value
Calculated Threshold Value: 138
Otsu’s Method–Video application
Connected Components Algorithm




Step 1: Input Original Image which must be a binary image
Step 2: For each pixel, examine surrounding 8 pixels
 If the pixel is neighboring, assign label 1
 If pixel is not neighboring, assign label 0
Step 3: Continue to check each pixel line by line until entire
image is checked, resulting in a matrix of 1’s and 0’s
Step 4: Convert image back to rgb in order to display
components in colors.
Connected Components
Algorithm—Video Application
Analysis

Expand connected components program to count the
number of pixels in a cell undergoing pre-attachment
behavior and plot this value over time
Frame 85
2000
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Frame 41
0
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Total Number of Pixels in
connected component
frame
Total Number of Pixels vs. Time
Time (Video Frame Number)
Analysis cont.
Expand program to compute average grayscale value of a
dancing cell over time and examine the averages and
standard deviations of these values over time per frame.
180
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20
0
Average Grayscale Value vs. Time
Dancing Cells
Attached Cell
Apoptotic Cell
1
5
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Average Grayscale Value

Time (Video Frame Number)
Analysis cont.
Grayscale Standard Deviation vs. Time
70
50
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10
Dancing Cells
Attached Cell
Apoptotic Cell
0
1
5
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Grayscale Standard Deviation
60
Time (Video Frame Number)
Conclusions
A preliminary calculation of the changes in pixel count for a
cell undergoing pre-attachment behavior displays no periodic
behavior. Pixel count data is also consistent with our
hypothesis of a high pixel count for cells in dancing and a low
count for a cell in attachment.
 The graphical analysis shows that the cells in the dancing,
attached, and unattached states have very distinct average
grayscale values per frame over the course of the video and
can potentially be automatically differentiated using this
property
 However, the standard deviations of these values for the
different stem cell states have a more inconsistent pattern
which results in interference with each other
 The average and standard deviation values can be combined
using pattern recognition techniques

Acknowledgements

I would like to extend much gratitude to
 Dr. Bir Bhanu for his guidance and advice
 Benjamin Guan for his willingness to teach
 Jun Wang for the opportunity this summer to do
research
 Talbot Lab—Dr. Prue Talbot and Sabrina Lin for
growing and filming the stem cells
 Shubham Debna and Lindsay Kulkin for their
collaboration
 Other members of the BRITE program and C.R.I.S.