Analyze OMERO timelapse images using the TrackMate API

In this example we open an image stored in an OMERO server and use the TrackMate API to analyze it. One of the advantages of this approach over the User interface workflow is that the generated track can be saved as OMERO ROIs.


First, we will show how to use the TrackMate API and the Scripting editor of Fiji.

We will show:

  • How to connect to OMERO using the JAVA API.

  • How to retrieve an image.

  • How to open the image using Bio-Formats.

  • How to create a TrackMate model using its API.

  • How to save the tracks as polygon ROIs in OMERO.


  • Install Fiji on the local machine with the OMERO.insight-ij plugin, version 5.5.10 or higher. The installation instructions can be found at here.



In this section, we go through the steps required to analyze the data. The script used in this document is tracking.groovy. The script follows similar steps than the ones used via the User Interface, see Analyze OMERO timelapse images using the TrackMate User Interface.

One advantage of the scripting approach is that we can save the generated tracks as ROIs in an OMERO server.

  1. Launch Fiji.

  2. Open File > New > Script....

  3. Select Groovy as a language.

  4. Copy the content of tracking.groovy in the text area.

  5. A dialog will pop up. Enter the credentials to connect to the server and select an Image. If you are not using websockets i.e. no wss in front of the host name, the port value needs to be changed to 4064.

  6. Click Run.

  7. Go back to OMERO.web to visualize the tracks. Double-click on the image in OMERO.web to open it in OMERO.iviewer.

  8. Click on the ROI tab and observe that you now have ROIs under which there are Shapes. Each ROI is a collection of shapes. The ROI corresponds to a track in Trackmate. There is always one polyline shape in each ROI which represents the track. The other, elliptical shapes in the same ROI represent the tracked spots.

  9. Play the timelapse video in OMERO.iviewer.

  10. Go to the Info tab, and in the Open with: line click on OMERO.figure. In OMERO.figure, add the Tracks and ellipses to the panel by selecting the appropriate ROIs in the Labels tab of OMERO.figure.


Script’s description

Import packages needed:

import java.awt.Color
import java.util.ArrayList

import omero.gateway.Gateway
import omero.gateway.LoginCredentials
import omero.gateway.SecurityContext
import omero.gateway.facility.BrowseFacility
import omero.gateway.facility.ROIFacility
import omero.gateway.model.EllipseData
import omero.gateway.model.PolylineData
import omero.gateway.model.ROIData
import omero.log.SimpleLogger
import omero.model.PolylineI
import static omero.rtypes.rstring

import ij.IJ

import fiji.plugin.trackmate.Spot
import fiji.plugin.trackmate.Settings
import fiji.plugin.trackmate.Model
import fiji.plugin.trackmate.SelectionModel
import fiji.plugin.trackmate.TrackMate
import fiji.plugin.trackmate.detection.DetectorKeys
import fiji.plugin.trackmate.detection.DogDetectorFactory
import fiji.plugin.trackmate.tracking.sparselap.SparseLAPTrackerFactory
import fiji.plugin.trackmate.tracking.LAPUtils
import fiji.plugin.trackmate.visualization.hyperstack.HyperStackDisplayer
import fiji.plugin.trackmate.features.track.TrackSpeedStatisticsAnalyzer

Connect to the server. It is also important to close the connection again to clear up potential resources held on the server. This is done in the disconnect method:

def connect_to_omero() {
    "Connect to OMERO"

    credentials = new LoginCredentials()
    simpleLogger = new SimpleLogger()
    gateway = new Gateway(simpleLogger)
    return gateway

def disconnect(gateway) {

Load the image from the server:

def get_image(gateway, image_id) {
    "Retrieve the image"
    browse = gateway.getFacility(BrowseFacility)
    user = gateway.getLoggedInUser()
    ctx = new SecurityContext(user.getGroupId())
    return browse.getImage(ctx, image_id)

Read the binary data using Bio-Formats:

def open_image_plus(host, port, username, password, group_id, image_id) {
    "Open the image using the Bio-Formats Importer"

    StringBuilder options = new StringBuilder()
    options.append("location=[OMERO] open=[omero:server=")
    options.append("] ")
    options.append("windowless=true view=Hyperstack ")
    IJ.runPlugIn("loci.plugins.LociImporter", options.toString())

Create a tracking model using the TrackMate API:

def create_tracker(imp) {
    "Create the trackmate model for the specified ImagePlus object"
    // Instantiate model object
    model = new Model()

    // Prepare settings object
    settings = new Settings()
    // Configure detector
    settings.detectorFactory = new DogDetectorFactory()
    settings.detectorSettings.put(DetectorKeys.KEY_DO_SUBPIXEL_LOCALIZATION, true)
    settings.detectorSettings.put(DetectorKeys.KEY_RADIUS, new Double(2.5))
    settings.detectorSettings.put(DetectorKeys.KEY_TARGET_CHANNEL, 1)
    settings.detectorSettings.put(DetectorKeys.KEY_THRESHOLD, new Double(5.0))
    settings.detectorSettings.put(DetectorKeys.KEY_DO_MEDIAN_FILTERING, false)
    // Configure tracker
    settings.trackerFactory = new SparseLAPTrackerFactory()
    settings.trackerSettings = LAPUtils.getDefaultLAPSettingsMap()
    settings.trackerSettings['LINKING_MAX_DISTANCE'] = new Double(10.0)
    settings.trackerSettings['GAP_CLOSING_MAX_DISTANCE'] = new Double(10.0)
    settings.trackerSettings['MAX_FRAME_GAP'] = 3

    // Add the analyzers for some spot features
    settings.addSpotAnalyzerFactory(new SpotIntensityAnalyzerFactory())
    settings.addSpotAnalyzerFactory(new SpotContrastAndSNRAnalyzerFactory())

    // Add an analyzer for some track features, such as the track mean speed.
    settings.addTrackAnalyzer(new TrackSpeedStatisticsAnalyzer())
    settings.initialSpotFilterValue = 1

    // Instantiate trackmate
    trackmate = new TrackMate(model, settings)
    ok = trackmate.checkInput()
    if (!ok) {
        return null

    ok = trackmate.process()
    if (!ok) {
        return null
    // Display the results on top of the image
    selectionModel = new SelectionModel(model)
    displayer = new HyperStackDisplayer(model, selectionModel, imp)
    // The feature model, that stores edge and track features.
    fm = model.getFeatureModel()
    space_units = model.getSpaceUnits()
    time_units = model.getTimeUnits()
    return model

Convert the tracks into OMERO ROIs:

def convert_tracks(model, dx, dy) {
    "Convert the tracks into OMERO objects"
    rois = new ArrayList()
    tracks = model.getTrackModel().trackIDs(true)
    tracks.each() { track_id ->
        track = model.getTrackModel().trackSpots(track_id)
        roi = new ROIData()
        points = ""
        track.each() { spot ->
            sid = spot.ID()
            // Fetch spot features directly from spot.
            x = spot.getFeature('POSITION_X')/dx
            y = spot.getFeature('POSITION_Y')/dy
            r = spot.getFeature('RADIUS')
            z = spot.getFeature('POSITION_Z')
            t = spot.getFeature('FRAME')
            // Save spot as Point in OMERO
            ellipse = new EllipseData(x, y, r, r)
            ellipse.setZ((int) z)
            ellipse.setT((int) t)
            // set trackmate track ID and spot ID for later
            // set a default color
            settings = ellipse.getShapeSettings()
            points = points + x + ',' + y + ' '
        // Save the track
        points = points.trim()
        polyline = new PolylineI()
        pl = new PolylineData(polyline)
        // set a default color
        settings = pl.getShapeSettings()
    return rois

Save the converted tracks back to the OMERO server:

def save_rois(gateway, rois, image_id) {
    roi_facility = gateway.getFacility(ROIFacility)
    user = gateway.getLoggedInUser()
    ctx = new SecurityContext(user.getGroupId())
    results = roi_facility.saveROIs(ctx, image_id, user.getId(), rois)

In order the use the methods implemented above in a proper standalone script, Wrap it all up:

gateway = connect_to_omero()
exp = gateway.getLoggedInUser()
group_id = exp.getGroupId()

image = get_image(gateway, image_id)
open_image_plus(HOST, PORT, USERNAME, PASSWORD, group_id, image_id)
imp = IJ.getImage()
dx = imp.getCalibration().pixelWidth
dy = imp.getCalibration().pixelHeight
trackmate_model = create_tracker(imp)
if (trackmate_model == null) {
	print("unable to create the trackmate model")
} else {
	omero_rois = convert_tracks(trackmate_model, dx, dy)
    save_rois(gateway, omero_rois, image_id)