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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

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

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

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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

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Temporal signals of features

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Interphase Prophase Prometaphase Metaphase Anaphase Telophase

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

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PART I – TRACKING

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

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

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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

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Logistic Regression Gives a probability map:

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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

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Graph Cuts

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

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

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

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Tracking

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PART II – PHASE LABELLING

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Simple features Maximum Intensity: Interphase

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Simple features Maximum Intensity: Interphase Prophase

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Simple features Maximum Intensity: Interphase Prophase Prometaphase

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Simple features Maximum Intensity: Interphase Prophase Prometaphase Metaphase

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Simple features Maximum Intensity: Interphase Prophase Prometaphase Metaphase Anaphase

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Simple features Maximum Intensity: Interphase Prophase Prometaphase Metaphase Anaphase

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Simple features Maximum Intensity: Interphase Prophase Prometaphase Metaphase Anaphase Telophase

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

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Dynamic time warping Stretch signal onto labelled reference:

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Dynamic time warping Stretch signal onto labelled reference:

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Dynamic time warping Interphase Prophase Prometaphase Metaphase Anaphase Telophase Interphase

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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

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Features

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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:

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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

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

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Results Interphase Prophase Prometaphase Metaphase Anaphase Telophase

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Results

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Outputs

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

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

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QUESTIONS?

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