0
Tracking B and T cells
from 2-photon microscopy imaging
David Olivieri, Iván Gómez and Jose Faro
(University of Vigo)
www....
Outline
 Motivation for this work
 Stochastic method for tracking: SMC
• Theoretical aspects
• Some algorithm implementa...
Immune Response
 Understand complex details of immune response by understanding dynamics
 Several important questions re...
Germinal centers
 “Germinal centers” are the
sites of affinity maturation
 Anatomic structures (in lymph
nodes) where ma...
Dynamics
• In vivo Data:
• “2-photon Confocal microscoy” with fluorescence
excitation labelling
• Better elimination of ba...
Tracking in Videos
 Tracking is hard in general!
• Normally needs to be real time
• many interactions: background, camera...
How to Tracking cells
 Cell movement:
• Problems: Complex, overlaps, “random” component
• BUT, flourescence color is a st...
Software Components
Method
Stochastic tracking
 Sequential Monte Carlo (smc)
 Formulate tracking as an inference problem in the context
of a Hidden...
Chapman Kolmogorov Eq.
 Evolution of the state (inference):
• Using the Bayesian filtering distribution:
Current Object
S...
Quantities of the Model
 Prior Distribution:
• Initial distribution of object states
 Evolution Model:
• How objects mov...
Prior distribution
 User input determines the object initial position
object
Iván Gómez Conde
Initial selection
of cells ...
Evolution model
Evolution Model (second-
order, auto-regressive
dynamical model)
SMC Method
p(xt | xt -1)
Likelihood function
 Likelihood Model (Distance metric):
Iván Gómez Conde
SMC Method
p(yt | xt )
SMC algorithm (summary)
1. Determine initial regions (roi) to track.
o From roi, store reference histogram
(each node)
2. ...
SMC Resampling
 Resampling, we
change weights
Iván Gómez Conde
SMC Method
SMC Tracking pseudocode
Iván Gómez Conde
SMC Method
Results: simple animation
 Showing each particle  Showing tracks of max L
Iván Gómez Conde
Results
Tracking Accuracy
Iván Gómez Conde
Results
Time Performance
Iván Gómez Conde
Results
Cells from Simulation
Iván Gómez Conde
Results
(simulation courtesy of J. Carneiro, T. Macedo, Instituto
Gulbenkian de Cie...
Cells Simulation: Ambiguities
Iván Gómez Conde
Results
 “unstructured” SMC leads to ambiguities
 Imagine two cells stick...
Cell Ambiguities: “Present” work
Iván Gómez Conde
Results
 Possible Solution: make constraints between particles
 Conser...
2-photon Microscopy videos
Iván Gómez Conde
Results
(videos courtesy of C.Allen, et.al
Science, 2006)
Conclusions
 SMC is a promising technique for tracking cells
o Relatively easy to implement and flexible
o Can use color ...
Many thanks for your
attention
Upcoming SlideShare
Loading in...5
×

Cell Tracking!

93

Published on

Tracking B cells from two-photon microscopy images using Sequential Monte Carlo

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
93
On Slideshare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Cell Tracking!"

  1. 1. Tracking B and T cells from 2-photon microscopy imaging David Olivieri, Iván Gómez and Jose Faro (University of Vigo) www.milegroup.net
  2. 2. Outline  Motivation for this work  Stochastic method for tracking: SMC • Theoretical aspects • Some algorithm implementation details  Results: • From simulations and animations • From real microscopy data of 2D cell motility  Conclusions (present and future work) Iván Gómez Conde
  3. 3. Immune Response  Understand complex details of immune response by understanding dynamics  Several important questions related to affinity maturation process  Cell activity and interactions Iván Gómez Conde Low Affinity Low Affinity High Affinity Activation Produces antibodies Motivation
  4. 4. Germinal centers  “Germinal centers” are the sites of affinity maturation  Anatomic structures (in lymph nodes) where massive proliferation of B-cells occur  Complex interactions between B and Th cells; spatial zones  Understanding dynamics in gernminal center ; better understand mechanisms of immune response Iván Gómez Conde Motivation Confocal microscopy image of GC:T-cells blue, B-cells green (photo courtesy of I. Wollenberb, IMM, Universidad de Lisboa Portugal y J. Faro, Fac. Biología, Universidad de Vigo)
  5. 5. Dynamics • In vivo Data: • “2-photon Confocal microscoy” with fluorescence excitation labelling • Better elimination of background Iván Gómez Conde Motivation  Dynamics is important! • B and T cell motility in germinal centers give information of function • Useful for “Inmunologic modeling” (input & validation)
  6. 6. Tracking in Videos  Tracking is hard in general! • Normally needs to be real time • many interactions: background, camera… • Methods: frame diff, homology, optical flow, particle filters  What can be learned from tracking objects? • Tracking cells is particularly difficult • Cells change shape, disappear, and stick to eachother. • complex background, Iván Gómez Conde Method
  7. 7. How to Tracking cells  Cell movement: • Problems: Complex, overlaps, “random” component • BUT, flourescence color is a strong feature to track • We propose “Stochastic color based tracking”: • SMC (stochastic monte carlo) Iván Gómez Conde Method
  8. 8. Software Components Method
  9. 9. Stochastic tracking  Sequential Monte Carlo (smc)  Formulate tracking as an inference problem in the context of a Hidden Markov Model (HMM)  Observations (from image data)  Hidden States (object location, scale, …) Yt Yt+1 Xt Xt+1 SMC Method
  10. 10. Chapman Kolmogorov Eq.  Evolution of the state (inference): • Using the Bayesian filtering distribution: Current Object State Observation Model Previous Object State Evolution Model SMC Method
  11. 11. Quantities of the Model  Prior Distribution: • Initial distribution of object states  Evolution Model: • How objects move between frames  Likelihood Function: • The probability of state x given the observation y Iván Gómez Conde p(x0) p(xt | xt -1) p(yt | xt ) SMC Method
  12. 12. Prior distribution  User input determines the object initial position object Iván Gómez Conde Initial selection of cells by the user SMC Method p(x0)
  13. 13. Evolution model Evolution Model (second- order, auto-regressive dynamical model) SMC Method p(xt | xt -1)
  14. 14. Likelihood function  Likelihood Model (Distance metric): Iván Gómez Conde SMC Method p(yt | xt )
  15. 15. SMC algorithm (summary) 1. Determine initial regions (roi) to track. o From roi, store reference histogram (each node) 2. Get image samples along trajectory of cell o Determined from the dynamics (position, velocity, update) o Obtain histograms of roi; compare with reference; keep best 3. Reorder the distribution for next sampling Iván Gómez Conde SMC Method
  16. 16. SMC Resampling  Resampling, we change weights Iván Gómez Conde SMC Method
  17. 17. SMC Tracking pseudocode Iván Gómez Conde SMC Method
  18. 18. Results: simple animation  Showing each particle  Showing tracks of max L Iván Gómez Conde Results
  19. 19. Tracking Accuracy Iván Gómez Conde Results
  20. 20. Time Performance Iván Gómez Conde Results
  21. 21. Cells from Simulation Iván Gómez Conde Results (simulation courtesy of J. Carneiro, T. Macedo, Instituto Gulbenkian de Ciencia, Portugal)
  22. 22. Cells Simulation: Ambiguities Iván Gómez Conde Results  “unstructured” SMC leads to ambiguities  Imagine two cells sticking to each other…  Just based on color, particles will sample entire region  Not sure which cell is which after contact
  23. 23. Cell Ambiguities: “Present” work Iván Gómez Conde Results  Possible Solution: make constraints between particles  Conserve area and distance; & non-overlap condition Node particle Constraint Preliminary results are promising! Modify the Weights to include constraints
  24. 24. 2-photon Microscopy videos Iván Gómez Conde Results (videos courtesy of C.Allen, et.al Science, 2006)
  25. 25. Conclusions  SMC is a promising technique for tracking cells o Relatively easy to implement and flexible o Can use color histogram or shape! o Easily extended to handle 3D image stacks o Stochastic noise can be controlled o Present and Future: o Extend to Constrained SMC can solve ambiguities o Implementation of system of “constrained particles” for each node Iván Gómez Conde
  26. 26. Many thanks for your attention
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×