Dynamic Time Warping for Automated Cell Cycle Labelling

Dynamic Time Warping for Automated Cell Cycle LabellingA. El-Labban, A. ZissermanUniversity of OxfordY. Toyoda, A. Bird, A. HymanMax Planck Institute of Molecular Cell Biology and GeneticsObjectivesSegment and track mitotic cells

Label mitotic phases

Fully automated system

InterphaseProphasePrometaphaseMetaphaseAnaphaseTelophaseData3D time lapse image stacks

Use max intensity z-projections

1-5 minute temporal resolution

0.2 micron xy-resolution

3ApproachExisting approaches (e.g. Harder et al. 2009, Held et al. 2010 [CellCognition]):Track cellsLabel cell cycle phase frame-by-frameSmooth result with HMM (CellCognition)

Our Approach:Track cellsLabel all frames by using temporal signals of featuresTemporal signals of features

Temporal signals of featuresInterphaseProphasePrometaphaseMetaphaseAnaphaseTelophase

OverviewPart ITrack cells in videos

Part IILabel mitotic phases

Part I TrackingTracking Tracking by detectionDetect first, then associate objectsHere we use detection by classification.

Segmentation: Our approachLogistic regression classifier

Graph Cuts

Input imageProbability mapBinary mapLogistic regression

classifierGraph CutLogistic Regression ClassifierFeature: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 RegressionGives a probability map:

Graph Cuts

Probability from Logistic Regression ClassifierGradient dependent pairwise termUses local neighbourhood information to make decisionsPairwise term penalises different labels for adjacent pixelsGraph Cuts

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

Tracking

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

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

Tracking

Part II Phase Labelling

Simple featuresMaximum Intensity:

Interphase

Simple featuresMaximum Intensity:

InterphaseProphase21

Simple featuresMaximum Intensity:

InterphaseProphasePrometaphase

Simple featuresMaximum Intensity:

InterphaseProphasePrometaphaseMetaphase

Simple featuresMaximum Intensity:

InterphaseProphasePrometaphaseMetaphaseAnaphase

Simple featuresMaximum Intensity:

InterphaseProphasePrometaphaseMetaphaseAnaphase

Simple featuresMaximum Intensity:

InterphaseProphasePrometaphaseMetaphaseAnaphaseTelophaseReference signalAverage over training set (1 standard deviation shaded):

Dynamic time warpingStretch signal onto labelled reference:

Dynamic time warpingStretch signal onto labelled reference:

Dynamic time warping

InterphaseProphasePrometaphaseMetaphaseAnaphaseTelophaseInterphaseDynamic time warpingFind a cost matrix of pairwise distances between points on the two signalsFind minimum cost path through matrix

Test SignalReference SignalFeatures

Hidden Markov ModelHidden states, xMitotic phases

Observations, yFeatures

Transition probabilities, aFrom one phase to the next

Emission probabilities, bOf features having a given value in a given phaseImage: http://en.wikipedia.org/wiki/Hidden_Markov_model33Hidden Markov ModelDTW essentially a special case of HMMEasy to extend approachCan add other classes e.g. cell deathSplit phases into sub-phases to account for variation

34Experiments and Data54 movies

119 mitotic tracks

27 movies (61 tracks) training, 27 movies (58 tracks) testingResultsInterphaseProphasePrometaphaseMetaphaseAnaphaseTelophase

Results

Outputs

OutputsSynopsis video1 of mitotic cells

Aligned to start of anaphase1Rav-Acha et al., 2006

ConclusionsNovel approach to cell cycle phase labelling

Utilises temporal context

Extendable with HMM Questions?