analysis in single-cell fluorescence microscopy

molassesitalianAI and Robotics

Nov 6, 2013 (3 years and 11 months ago)

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SGN
-
9406 Signal Processing Graduate Seminar IV

Image segmentation and dynamic lineage
analysis in single
-
cell fluorescence microscopy


Quanli Wang, Jarad Niemi, Chee
-
Meng Tan, Lingchong You and Mike West



Muhammad Farhan (210911)



Outline



Introduction


Image preprocessing and hybrid image generation


Filters for hybrid images


Iterative mask
-
based cell segmentation


Cell tracking and lineage reconstruction


Results


Comparison and discussion


Conclusion










2

SGN
-
9406 Signal Processing Graduate
Seminar IV

Introduction


Time
-
lapse

movies

and

analysis

of

dynamics

of

gene

expression

at

single
-
cell

level
.


Challenge

is

automatic

segmentation

and

tracking

of

individual

cells
.


No

universal

algorithm

applicable

across

cell

types
.


Major

goal

in

algorithm

design

and

s/w

development
:

Set

of

tools

for

extracting

cell

like

objects

from

many

different

types

of

images
.

3

SGN
-
9406 Signal Processing Graduate
Seminar IV

Introduction (Contd..)


Problems with existing algorithms:


Initial

cell

identity

and

location

marker

needed

for

segmentation!!


Tracking

by

minimizing

global

energy

functions

typically

don’t

consider

locally

clustered

cells
.


Solutions to problems


Iterative

extraction

of

cells

by

using

hybrid

grey
-
scale/black
-
white

images

i
.
e

gradual

conversion

of

grey
-
scale

image

to

black
-
white

mask
.


Using

neighborhood

cell

information

to

compute

numerical

likelihood

scores

for

cell

identity

between

each

pair

of

consecutive

time

steps
.



Generation

of

frame
-
frame

correspondences

between

cells

and

lineage

map
.





4

SGN
-
9406 Signal Processing Graduate
Seminar IV

Introduction (Contd..)


Commercial

s/w

for

general

purpose

image

analysis
:

Imaris,Amira,Volocity,Metamorph,Matlab

Image

processing

toolbox
.


Public
-
domain

tools

contain

Image
-
J

for

general

purpose

image

analysis

whereas

CellProfiler,

Cell
-
ID,CellTracker

and

GoFigure

are

for

segmentation

and

tracking

in

more

specific

areas
.


5

SGN
-
9406 Signal Processing Graduate
Seminar IV

Image preprocessing and hybrid image
generation


Grey
-
scale

image

is

divided

into

background,border

and

undecided
.


Modified non
-
linear range filter and dilation help identify background.


Extended high
-
pass filter identifies border regions.


Undecided regions need to be further classified.


Special

meaning

to

largest

values

in

grey
-
scale

image

creates

the

hybrid

grey
-
scale/black
-
white

image
.


Modification is required in normal filters to work for hybrid images.


Masks are used to exclude background pixels in border identification.

6

SGN
-
9406 Signal Processing Graduate
Seminar IV

Filters for hybrid images


A class of mask
-
based filters can be designed for hybrid images


Suppose, a hybrid image is given by
H = (I,M)

with
I

being grey
-
scale image and
M

being the black
-
white mask. If a function
H’ = F(H) = F(I,M) = (I’,M’)

also gives a
hybrid image, then
F

is a hybrid filter if


F(I,M) = F(I.*M , M)

, where
.*

is element
-
wise product



Hybrid range filters(HRF) is a specific class of hybrid filters given by





where is the given image pixel, is the structuring element and


where is the set intersection opertator.


With extension of above definition, hybrid mean,median or rank
filters can be designed.

7

SGN
-
9406 Signal Processing Graduate
Seminar IV

Iterative mask
-
based cell segmentation


Hybrid image is processed with combination of hybrid filters to
erode the undecided regions, followed by hybrid dilation and
smoothing.


Iterative processing is performed to gradually change the
associated mask until a stopping rule is met. Final mask is the
resulting segmented image.


Cell object modeling and selection at the end of each iteration
enhances the above obtained result.


8

SGN
-
9406 Signal Processing Graduate
Seminar IV

Iterative mask
-
based cell segmentation
(Contd..)


Disconnected segmented regions


referred to as blobs


in the
binary mask are
labeled
, evaluated, scored.


All blobs are classified as cells or undecided,
based on the object
shape model,

where undecided ones are subject to further
segmentation.


Useful especially when the cells exhibit diversity in scale, e.g., cells
differing in diameter, in which case cells at different scales will be
picked up at different iterations.


9

SGN
-
9406 Signal Processing Graduate
Seminar IV

Cell tracking and lineage reconstruction


Two
-
step

algorithm
:

Construction

of

score

matrices

and

Optimization

algorithm

to

obtain

the

frame
-
to
-
frame

correspondence

matrices
.



Calculation

of

geometric

overlapping

scores(>

0
.
2

is

valid

here)

for

the

pair

to

construct

forward

and

backward

scores
.


A

cell

i

is

in

the

neighborhood

of

a

cell

j

if

they

overlap

at

least

once

if

we

shift

cell

i

within

given

pixels

p
.


Computation

of

a

pair

of

neighborhood

scores

over

the

neighborhood

of

each

cell

using

the

overlapping

scores
.



Repeatedly

shifitng

one

image

slightly

and

recalculating

overlapping

and

neighborhood

scores

for

cell

pairs
.




10

SGN
-
9406 Signal Processing Graduate
Seminar IV

Cell tracking and lineage reconstruction
(Contd..)


Combination

of

score

matrices

and

transformation

into

a

0
-
1

correspondence

matrix
.


Dot

product

of

score

matrices

to

get

combined

score

matrix
.


Calculation

of

row

and

column

maxima

in

combined

score

matrix
.


Locating

values

that

are

both

row

and

column

maxima

and

assigning

matches

for

corresponding

cell

pairs
.


Use

of

backward

score

matrix

for

selection

of

two

best

scores

for

assigning

the

matches
.


Reconstruction

of

cell

lineage

trees

by

linking

the

sequence

of

correspondence

matrices
.




11

SGN
-
9406 Signal Processing Graduate
Seminar IV

Results

12

SGN
-
9406 Signal Processing Graduate
Seminar IV

Results(Contd..)

13

SGN
-
9406 Signal Processing Graduate Seminar IV

Results(Contd..)

14

SGN
-
9406 Signal Processing Graduate
Seminar IV

Results(Contd..)

15

SGN
-
9406 Signal Processing Graduate
Seminar IV

Results(Contd..)

16

SGN
-
9406 Signal Processing Graduate
Seminar IV


It
is shown that the neighborhood score based tracking method
provides a much sharper score matrix with substantially improved
cell correspondences.


The
algorithm reconstructs almost 98% of actual tracks from the
last frame back to the first frame in the movie, as compared to 58%
by the one in
Sigal

et al.


The
analysis was a direct application of the automated method
applied to the data with no additional parameter tuning.


Robust approach because of using common features and avoiding
modeling

cell growth transformation.


Advantageous to use hybrid images and filters.


Comparison and discussion

17

SGN
-
9406 Signal Processing Graduate
Seminar IV


One particular implementation of overall strategy and resulting
algorithm is presented.


Improved segmentation results, or more efficient algorithms, may
be achieved by tailoring the workflow to more specific features of a
data set.


The
emphasis is on robustness and portability.


Conclusion

18

SGN
-
9406 Signal Processing Graduate
Seminar IV

QUESTIONS ?

Thank You!!

19

SGN
-
9406 Signal Processing Graduate
Seminar IV