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Dynamic Time Warping for Automated Cell Cycle Labelling. A. El-Labban, A. Zisserman University of Oxford. Y. Toyoda, A. Bird, A. Hyman Max Planck Institute of Molecular Cell Biology and Genetics. Objectives. Segment and track mitotic cells Label mitotic phases Fully automated system.

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dynamic time warping for automated cell cycle labelling

Dynamic Time Warping for Automated Cell Cycle Labelling

A. El-Labban, A. Zisserman

University of Oxford

Y. Toyoda, A. Bird, A. Hyman

Max Planck Institute of Molecular Cell Biology and Genetics

objectives
Objectives
  • Segment and track mitotic cells
  • Label mitotic phases
  • Fully automated system

Interphase

Prometaphase

Anaphase

Telophase

Prophase

Metaphase

slide3
Data
  • 3D time lapse image stacks
  • Use max intensity z-projections
  • 1-5 minute temporal resolution
  • 0.2 micron xy-resolution
approach
Approach
  • Existing approaches (e.g. Harder et al. 2009, Held et al. 2010 [CellCognition]):
    • Track cells
    • Label cell cycle phase frame-by-frame
    • Smooth result with HMM (CellCognition)
  • Our Approach:
    • Track cells
    • Label all frames by using temporal signals of features
temporal signals of features1
Temporal signals of features

Anaphase

Interphase

Prometaphase

Telophase

Prophase

Metaphase

overview
Overview
  • Part I
    • Track cells in videos
  • Part II
    • Label mitotic phases
tracking
Tracking
  • Tracking by detection
    • Detect first, then associate objects
    • Here we use detection by classification.
segmentation our approach
Segmentation: Our approach
  • Logistic regression classifier
  • Graph Cuts

Logistic regression

classifier

Graph Cut

Input image

Probability map

Binary map

logistic regression classifier
Logistic Regression Classifier
  • Feature:
    • 10 bin intensity histogram in 5x5 window around pixel
    • Non-uniform bins
    • Get local neighbourhood information as opposed to single pixel
    • Histogram gives rotational invariance
logistic regression
Logistic Regression
  • Gives a probability map:
graph cuts
Graph Cuts

Gradient dependent pairwise term

Probability from Logistic Regression Classifier

  • Uses local neighbourhood information to make decisions
  • Pairwise term penalises different labels for adjacent pixels
tracking1
Tracking
  • Associate objects based on distance between centroids in consecutive frames.
  • Easy given segmentation and slow movement of cells.
tracking2
Tracking
  • Associate objects based on distance between centroids in consecutive frames.
  • Easy given segmentation and slow movement of cells.
tracking3
Tracking
  • Associate objects based on distance between centroids in consecutive frames.
  • Easy given segmentation and slow movement of cells.
simple features
Simple features
  • Maximum Intensity:

Interphase

simple features1
Simple features
  • Maximum Intensity:

Interphase

Prophase

simple features2
Simple features
  • Maximum Intensity:

Interphase

Prometaphase

Prophase

simple features3
Simple features
  • Maximum Intensity:

Interphase

Prometaphase

Prophase

Metaphase

simple features4
Simple features
  • Maximum Intensity:

Anaphase

Interphase

Prometaphase

Prophase

Metaphase

simple features5
Simple features
  • Maximum Intensity:

Anaphase

Interphase

Prometaphase

Prophase

Metaphase

simple features6
Simple features
  • Maximum Intensity:

Anaphase

Interphase

Prometaphase

Telophase

Prophase

Metaphase

reference signal
Reference signal
  • Average over training set (±1 standard deviation shaded):
dynamic time warping
Dynamic time warping
  • Stretch signal onto labelled reference:
dynamic time warping1
Dynamic time warping
  • Stretch signal onto labelled reference:
dynamic time warping2
Dynamic time warping

Anaphase

Interphase

Prometaphase

Interphase

Telophase

Prophase

Metaphase

dynamic time warping3
Dynamic time warping
  • Find a cost matrix of pairwise distances between points on the two signals
  • Find minimum cost path through matrix

Test Signal

Reference Signal

features
Features
  • Use 3 features and their gradients at two different scales:
    • Maximum intensity
    • Area
    • Compactness ( )
hidden markov model
Hidden Markov Model
  • Hidden states, x
    • Mitotic phases
  • Observations, y
    • Features
  • Transition probabilities, a
    • From one phase to the next
  • Emission probabilities, b
    • Of features having a given value in a given phase

Image: http://en.wikipedia.org/wiki/Hidden_Markov_model

hidden markov model1
Hidden Markov Model
  • DTW essentially a special case of HMM
  • Easy to extend approach
  • Can add other classes e.g. cell death
  • Split phases into sub-phases to account for variation
experiments and data
Experiments and Data
  • 54 movies
  • 119 mitotic tracks
  • 27 movies (61 tracks) training, 27 movies (58 tracks) testing
results
Results

Interphase

Prophase

Prometaphase

Metaphase

Anaphase

Telophase

outputs1
Outputs
  • Synopsis video1 of mitotic cells
  • Aligned to start of anaphase

1Rav-Acha et al., 2006

conclusions
Conclusions
  • Novel approach to cell cycle phase labelling
  • Utilises temporal context
  • Extendable with HMM
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