total | |||||
20 | 20 | 20 | 20 | 20 | |
20 | 20 | 20 | 20 | 20 | |
20 | 20 | 20 | 20 | 20 | |
24 | 24 | 14 | 17 | 20 | |
32 | 27 | 9 | 14 | 17 | |
33 | 30 | 5 | 10 | 15 | |
53 | 40 | 1 | 4 | 14 |
Microscopy imaging of plant cells allows the elaborate analysis of sub-cellular motions of organelles. The large video data set can be efficiently analyzed by automated algorithms. We develop a novel, data-oriented algorithm, which can track organelle movements and reconstruct their trajectories on stacks of image data. Our method proceeds with three steps: (ⅰ) identification, (ⅱ) localization, and (ⅲ) linking. This method combines topological data analysis and Ensemble Kalman Filtering, and does not assume a specific motion model. Application of this method on simulated data sets shows an agreement with ground truth. We also successfully test our method on real microscopy data.
Citation: |
Figure 1.
The motion of organelles, during an experiment starting at
Figure 2.
Here,
Figure 4.
(a) shows
Figure 5.
Case I: The frame size is 320 by 320 pixels. Trajectories of 20 organelles are in red spanning from time
Figure 8.
Case I: Positions of organelles over time after adding perturbation
Figure 9. Case II: The left shows the rough detection result, the right show the locations after correction. The red dots in the left penal and blue pentagons in the right penal are the original locations before Bayesian identification. The red pentagons in the right panel are the fitted location after Bayesian identification
Figure 12.
Case II: Four specific sets of trajectory reconstructions vs ground truth. Each panel shows reconstructions versus one true trajectory. The upper left is amplified from the area
Table 1. Case I: Table of detection result
total | |||||
20 | 20 | 20 | 20 | 20 | |
20 | 20 | 20 | 20 | 20 | |
20 | 20 | 20 | 20 | 20 | |
24 | 24 | 14 | 17 | 20 | |
32 | 27 | 9 | 14 | 17 | |
33 | 30 | 5 | 10 | 15 | |
53 | 40 | 1 | 4 | 14 |
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