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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.

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Presentation on theme: "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."— Presentation transcript:

1 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

2 Objectives  Segment and track mitotic cells  Label mitotic phases  Fully automated system Interphase Prophase Prometaphase Metaphase Anaphase Telophase

3 Data  3D time lapse image stacks  Use max intensity z-projections  1-5 minute temporal resolution  0.2 micron xy-resolution

4 Approach  Existing approaches (e.g. Harder et al. 2009, Held et al [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

5 Temporal signals of features

6 Interphase Prophase Prometaphase Metaphase Anaphase Telophase

7 Overview  Part I  Track cells in videos  Part II  Label mitotic phases

8 PART I – TRACKING

9 Tracking  Tracking by detection  Detect first, then associate objects  Here we use detection by classification.

10 Segmentation: Our approach  Logistic regression classifier  Graph Cuts Input image Probability map Binary map Logistic regression classifier Graph Cut

11 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

12 Logistic Regression Gives a probability map:

13 Graph Cuts Probability from Logistic Regression Classifier Gradient dependent pairwise term Uses local neighbourhood information to make decisions Pairwise term penalises different labels for adjacent pixels

14 Graph Cuts

15 Tracking Associate objects based on distance between centroids in consecutive frames. Easy given segmentation and slow movement of cells.

16 Tracking Associate objects based on distance between centroids in consecutive frames. Easy given segmentation and slow movement of cells.

17 Tracking Associate objects based on distance between centroids in consecutive frames. Easy given segmentation and slow movement of cells.

18 Tracking

19 PART II – PHASE LABELLING

20 Simple features  Maximum Intensity: Interphase

21 Simple features  Maximum Intensity: Interphase Prophase

22 Simple features  Maximum Intensity: Interphase Prophase Prometaphase

23 Simple features  Maximum Intensity: Interphase Prophase Prometaphase Metaphase

24 Simple features  Maximum Intensity: Interphase Prophase Prometaphase Metaphase Anaphase

25 Simple features  Maximum Intensity: Interphase Prophase Prometaphase Metaphase Anaphase

26 Simple features  Maximum Intensity: Interphase Prophase Prometaphase Metaphase Anaphase Telophase

27 Reference signal  Average over training set (±1 standard deviation shaded):

28 Dynamic time warping  Stretch signal onto labelled reference:

29 Dynamic time warping  Stretch signal onto labelled reference:

30 Dynamic time warping Interphase Prophase Prometaphase Metaphase Anaphase Telophase Interphase

31 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

32 Features

33 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:

34 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

35 Experiments and Data  54 movies  119 mitotic tracks  27 movies (61 tracks) training, 27 movies (58 tracks) testing

36 Results Interphase Prophase Prometaphase Metaphase Anaphase Telophase

37 Results

38 Outputs

39  Synopsis video 1 of mitotic cells  Aligned to start of anaphase 1 Rav-Acha et al., 2006

40 Conclusions  Novel approach to cell cycle phase labelling  Utilises temporal context  Extendable with HMM

41 QUESTIONS?


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