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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. Segment and track mitotic cells Label mitotic phases Fully automated system.

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Dynamic Time Warping for Automated Cell Cycle Labelling

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  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 Prometaphase Anaphase Telophase Prophase Metaphase

  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. 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

  5. Temporal signals of features

  6. Temporal signals of features Anaphase Interphase Prometaphase Telophase Prophase Metaphase

  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 Logistic regression classifier Graph Cut Input image Probability map Binary map

  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 Gradient dependent pairwise term Probability from Logistic Regression Classifier • 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 Prometaphase Prophase

  23. Simple features • Maximum Intensity: Interphase Prometaphase Prophase Metaphase

  24. Simple features • Maximum Intensity: Anaphase Interphase Prometaphase Prophase Metaphase

  25. Simple features • Maximum Intensity: Anaphase Interphase Prometaphase Prophase Metaphase

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

  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 Anaphase Interphase Prometaphase Interphase Telophase Prophase Metaphase

  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 • Use 3 features and their gradients at two different scales: • Maximum intensity • Area • Compactness ( )

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

  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. Outputs • Synopsis video1 of mitotic cells • Aligned to start of anaphase 1Rav-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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