Fiji plugin -Particle Tracker 2D/3D

Transcription

1 Fiji plugin -Particle Tracker 2D/3D Math-Clinic Short Tutorial, November 19, 2014 Chong Zhang

2 Installation download: Old version available Video for installation Automatically install and update with Fiji Updater: Fiji -> Help -> Update Fiji -> Manage update sites -> add site: Tutorial (not updated!):

3 (Initially intended) Applications Tracking viruses (on the plasma membrane) trafficking along microtubules particles with strong intensity fluctuation, e.g. blinking objects, objects that move in and out of focus Diffusion particles Endosome (smooth motion) tracking (Sbalzarini & Koumoutsakos, 2005)

4 Steps Import -> Image Sequence (convert to 8bit??) Image -> Properties... Plugins -> Mosaic -> Particle Tracker 2D/3D Tune parameters Inspecting & storing results

5 Results

6 Processing details Image restoration: Background removal: average over 2*Radius+1 neighbourhood Denoising: Gaussian smoothing (2*Radius+1 kernel) Estimating particle locations: Local intensity maxima within Radius distance neighbourhood & upper specified intensity percentile in current frame (note: local maximasmay include noise and spurious (bright) points; assume local maximas are near true geometric center points of particles) Refinement by offset related to intensity-weighted centroid within Radius distance Non-particle discrimination: (clustering in feature space) Intensity moments orders: 0 th order: the total intensity of the particle; 2 nd order: the total intensity weighted by the squared distance from the centroid of the particle normalized by the total intensity, i.e. representing distribution of intensity Radius: slightly larger than the visible particle radius, but smaller than the smallest inter-particle separation Percentile: depends on brightness of particles of interest Cutoff: particle have similar appearance (across the whole movie), it can be set higher; otherwise, it should be small or zero.

7 Detection Radius: Approximate radius of the particles in the images in units of pixels. (The value should be slightly larger than the visible particle radius, but smaller than the smallest inter-particle separation.) Cutoff: The score cut-off for the non-particle discrimination. Percentile: brightpixels in the upper specified percentile of the image intensity distribution (per frame basis) are considered candidate Particles.

8 Detection example 3, 0, 0.1 3, 0, 0.6 6, 0, 0.6 3, 0, 2 3, 3, 2 3, 3, 0.6

9 Linking Link Range: The number of subsequent frames that is taken into account to determine the optimal correspondence matching. Displacement: The maximum number of pixelsas movement between two succeeding frames. Dynamics: type of motion Advanced options: Brownianmotion is the random motion of particles resulting from their collision with the quick atoms or molecules. The direction of the force of atomic bombardment is constantly changing, and at different times the particle is hit more on one side than another, leading to the seemingly random nature of the motion. Greedyalgorithm: follows the problem solving heuristic of making the locally optimal choice at each stage Hungarian algorithm: solves the assignment problem with minimal cost. Only accepts Link Range 1.

10 Linking example Configuration: Kernel radius: 3 Cutoff radius: 0.0 Percentile: 0.9 Displacement : 20.0 Linkrange : 1 Advanced options: Obj Feature: 0.33 Dynamics: 1.0 Hungarian Configuration: Kernel radius: 3 Cutoff radius: 0.0 Percentile: 0.9 Displacement : 20.0 Linkrange : 3 red line: "Gap" -the trajectory part is interpolated to handle occlusion, exit and entry of the particle. Advanced options: Obj Feature: 0.33 Dynamics: 1.0 Greedy

11 Additional notes / assumptions Small particles(compared to the length scale of background variations) Limited speed Short occlusions Most detected particles have similar intensity characteristics, i.e. form a cluster Exactly one physical particle producing a single point detection.

12 Example 1 Configuration: Kernel radius: 3 Cutoff radius: 0.0 Percentile: 0.9 Displacement : 20.0 Linkrange : 1 Advanced options: Obj Feature: 0.33 Dynamics: 1.0 Hungarian Plugin example image

13 Example 2 Configuration: Kernel radius: 3 Cutoff radius: 3.0 Percentile: 2.0 Displacement : 5.0 Linkrange : 2 EMBL master course example image

14 Example 3 Configuration: Kernel radius: 15 Cutoff radius: 0.0 Percentile: 7.0 Displacement : 10.0 Linkrange : 2 (Cell) division event gives one new tracking link for one of the children, and the other one (hopefully) keeps going with the parent link. Mitocheck example image

15 Example 4 Configuration: Kernel radius: 23 Cutoff radius: 0.0 Percentile: 3.0 Displacement : 30.0 Linkrange : 2 Image from P. Himmels

16 Preprocessing steps Deconvolution (A connected component in image may contain more than one particle/object, where deconvolved image may solve this.) Other particle enhancement steps (apart from the background removal and denoising steps provided)

17 Summary + Very few parameters to tune + Convenient visualization (for presentations) + Could work on non-particle like objects (to some extents) + Does not assume particles move along smooth curve, e.g. Brownian motion + Particles can have different motion modes, and with different speed (to some extents) + Also available as a Matlabtoolbox - Does not link (cell) division/ kissing -Two similar particles must always be separated by more than the distance they move per frame - Does not give segmentation, rather center points positions -Small objects (compared to background), roundish -Sensitive to detection, in the sense that it assumes one particle per detection point -Not available/flexible in postprocessing, e.g. manually correct some linkings -Might take some time for large particles datasets

18 References I.F. Sbalzarini, P. Koumoutsakos, Feature point tracking and trajectory analysis for video imaging in cell biology, Journal of structural biology, I.F. Sbalzarini, A MATLAB toolbox for virus particle tracking, ICoS Technical Report, 2007.