Digital Analysis of Carcinogenic Cell Culture Images
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The Institute for Medical BioMathematics (IMBM) . Supervisors: Prof. Agur Zvia, Shukron Ofir Students: Goldshtein Lilach, Kraus Roni Could a computerized algorithm be a reliable and sufficient tool for identifying and tracking a subpopulation of cancer stem cells in a culture of cells that adhere to each other? Cancer stem cells (CSC‟s) have enhanced proliferation ability and stability to conventional chemotherapy and therefore they maintain the tumor (Vande Woude et al., 2007). Manual protocols of cell count are invasive procedures that may influence the culture analysis. They also require growing of multiple cultures, since there is a need to dismantle the culture each time a measurement is done (Langdon, 2004). The main goals of the algorithm is to perform a cell count and to create a regional "thermal map" characterizing "hot" regions by the high frequency of cell divisions in them. Tracking these regions enables creating "quality-targets“ of CSC‟s for radiologists and for the precise administration of customized treatments. Area suspected to contain CSC‟s were classified as „hot zones‟, though not fully. Most rounded cells were classified as „hot zones‟ High sensitivity and low probability for false detecting. A 3rd degree polynomial fitting was the most suitable model (Faessel et al., 1999). The quality of the results is primarily affected by the segmentation results. Colony Separation 1 1 2 1 2 2 n n I I n i i n i i Segmentation The Watershed transform (Luc & Soille, 1991) splits the image into areas, based on topology of the image. Over-segmentation reduction- preprocessing Suppressing shallow minima points using H-minima (Banhart, 2008) transform. Oversegmentation reduction - postprocessing Reduction of impulse noise using a non linear Median filter. Noise reduction 1. “Islands” removal. 2. Removal of small areas that are close to the colony‟s edge. 3. Removal of boundaries with low height (Bieniechi, 2004). Classification K-means clustering (Spath, 1985) using a features vector containing: 2D standard deviation: Entropy: Homogeneity: Number of neighbors with relative area ~1. 1 1 1 2 mn I m i n j ij mn i i i p p S 1 2 log m i n j ij j i p H 1 1 1 i j i area A A A R A recursive split algorithm using variance. Time dependent analysis Curve fitting to the colony growth. Cell count sensitivity ≈ 86% Cell count false detection percentage ≈ 13% 0 50 100 150 200 250 300 1 2 3 4 5 6 7 8 x 10 5 Colony Size in time No. of frame No. of pixels y = 0.01*x 3 + 0.28*x 2 + 7.3e+002*x + 1.9e+005 Colony Size Cubic fitting r 2 =0.998 0 50 100 150 200 250 300 100 200 300 400 500 600 700 Cell Count in time No. of frame No. of cells y = - 5.6e-006*x 3 + 0.0053*x 2 + 0.5*x + 1.6e+002 Cell Count Cubic fitting r 2 =0.992 Colony separation Noise reduction Over-segmentation reduction Classification Cell count as function of time Colony size as function of time
Transcript of Digital Analysis of Carcinogenic Cell Culture Images
The Institute for Medical BioMathematics (IMBM) . Supervisors: Prof. Agur Zvia, Shukron Ofir
Students: Goldshtein Lilach, Kraus Roni
Could a computerized algorithm be a reliable and sufficient tool
for identifying and tracking a subpopulation of cancer stem cells in
a culture of cells that adhere to each other?
Cancer stem cells (CSC‟s) have enhanced proliferation ability and
stability to conventional chemotherapy and therefore they maintain
the tumor (Vande Woude et al., 2007).
Manual protocols of cell count are invasive procedures that may
influence the culture analysis. They also require growing of
multiple cultures, since there is a need to dismantle the culture
each time a measurement is done (Langdon, 2004).
The main goals of the algorithm is to perform a cell count and to
create a regional "thermal map" characterizing "hot" regions by the
high frequency of cell divisions in them. Tracking these regions
enables creating "quality-targets“ of CSC‟s for radiologists and for
the precise administration of customized treatments.
Area suspected to contain CSC‟s were classified as „hot
zones‟, though not fully.
Most rounded cells were classified as „hot zones‟
High sensitivity and low probability for false detecting.
A 3rd degree polynomial fitting was the most suitable
model (Faessel et al., 1999).
The quality of the results is primarily affected by the
segmentation results.
Colony Separation
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1
2
1
2
2
n
n
I
I
n
i
in
i
i
Segmentation
The Watershed transform (Luc & Soille, 1991) splits
the image into areas, based on topology of the image.
Over-segmentation reduction- preprocessing
Suppressing shallow minima points using
H-minima (Banhart, 2008) transform.
Oversegmentation reduction - postprocessing
Reduction of impulse noise using a non linear Median filter.
Noise reduction
1. “Islands” removal. 2. Removal of small areas that are close to
the colony‟s edge.
3. Removal of boundaries with low height (Bieniechi, 2004).
ClassificationK-means clustering (Spath, 1985) using a features vector containing:
2D standard deviation: Entropy:
Homogeneity: Number of neighbors with
relative area ~1.
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1 1
2
mn
Im
i
n
j
ij
mn
i
ii ppS1
2log
m
i
n
j
ij
ji
pH
1 1 1
i
ji
areaA
AAR
A recursive split algorithm using variance.
Before oversegmentation reduction
Before oversegmentation reduction
Time dependent analysis
Curve fitting to the colony growth.
Cell count sensitivity ≈ 86%
Cell count false detection
percentage ≈ 13%
0 50 100 150 200 250 3001
2
3
4
5
6
7
8x 10
5 Colony Size in time
No. of frame
No.
of
pix
els
y = 0.01*x3 + 0.28*x2 + 7.3e+002*x + 1.9e+005
Colony Size
Cubic fitting
r2=0.998
0 50 100 150 200 250 300100
200
300
400
500
600
700Cell Count in time
No. of frame
No.
of
cells
y = - 5.6e-006*x3 + 0.0053*x2 + 0.5*x + 1.6e+002
Cell Count
Cubic fitting
r2=0.992
Colony separation
Noise reduction
Over-segmentation reduction
Classification Cell count as function of time
Colony size as function of time
