SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter

Hirose, Osamu; Kawaguchi, Shotaro; Tokunaga, Tohru; Toyoshima, Yu; Teramoto, Takayuki; Kuge, Sayuri; Ishihara, Takeshi; Iino, Yuichi; Yoshida, Ryo

doi:10.1109/tcbb.2017.2782255

Cited by 22 publications

(11 citation statements)

References 20 publications

(28 reference statements)

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“…Note that there is also a lack of benchmark reference datasets with time‐lapse synthetic images; thus, tracking algorithm researchers are often left with manual expert annotation or expert revision of results . Nonetheless, we have recently noticed a few articles that have suggested new tracking algorithms and also their evaluation on time‐lapse synthetic datasets.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Virtual cell imaging: A review on simulation methods employed in image cytometry

et al. 2016

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The simulations of cells and microscope images thereof have been used to facilitate the development, selection, and validation of image analysis algorithms employed in cytometry as well as for modeling and understanding cell structure and dynamics beyond what is visible in the eyepiece. The simulation approaches vary from simple parametric models of specific cell components-especially shapes of cells and cell nuclei-to learning-based synthesis and multi-stage simulation models for complex scenes that simultaneously visualize multiple object types and incorporate various properties of the imaged objects and laws of image formation. This review covers advances in artificial digital cell generation at scales ranging from particles up to tissue synthesis and microscope image simulation methods, provides examples of the use of simulated images for various purposes ranging from subcellular object detection to cell tracking, and discusses how such simulators have been validated. Finally, the future possibilities and limitations of simulation-based validation are considered. © 2016 International Society for Advancement of Cytometry.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Virtual cell imaging: A review on simulation methods employed in image cytometry

et al. 2016

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“…The most simplistic approaches use nearest neighbor methods [ 12 , 13 ] or are based on overlap [ 14 , 15 ]. Bayesian filters like the Kalman filter [ 16 ], particle filter [ 17 – 19 ] or Bernoulli filter [ 20 , 21 ] have been adapted for cell tracking as well. Hybrid methods combine simplistic tracking methods, like nearest neighbors, with more sophisticated tracking methods [ 22 – 25 ].…”

Section: Introductionmentioning

confidence: 99%

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

2021

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Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.

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“…The most simplistic approaches use nearest neighbor methods [12, 13] or are based on overlap [14, 15]. Bayesian filters like the Kalman filter [16], particle filter [17–19] or Bernoulli filter [20, 21] have been adapted for cell tracking as well. Hybrid methods combine simplistic tracking methods, like nearest neighbors, with more sophisticated tracking methods [22–25].…”

Section: Introductionmentioning

confidence: 99%

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

Löffler

Scherr

Mikut

2021

Preprint

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Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We investigate the performance of our approach by simulating erroneous segmentation data, including false negatives, over- and under-segmentation errors, on 2D and 3D cell data sets. We compare our approach against three well-performing tracking algorithms from the Cell Tracking Challenge. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. Furthermore, in case of under-segmentation or a combination of segmentation errors our approach outperforms the other tracking approaches.

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SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter

Cited by 22 publications

References 20 publications

Virtual cell imaging: A review on simulation methods employed in image cytometry

Virtual cell imaging: A review on simulation methods employed in image cytometry

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction

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