On-line Tracking of Cells and Their Lineage from Time Lapse Video Data

Nguyen, Tran Thien Dat; Kim, Du Yong

doi:10.1109/iccais.2018.8570546

Cited by 5 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2(a). We focus on tracking the centroid of the cells and use the mitotic model in [24]. The parameters for the models and filters are set based on the characteristics of the dataset, the detection quality, and they are presented in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Biological Cell Tracking And Lineage Inference Via Random Finite Sets

Nguyen

Shim

Kim

2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

View full text Add to dashboard Cite

Automatic cell tracking has long been a challenging problem due to the uncertainty of cell dynamic and observation process, where detection probability and clutter rate are unknown and time-varying. This is compounded when cell lineages are also to be inferred. In this paper, we propose a novel biological cell tracking method based on the Labeled Random Finite Set (RFS) approach to study cell migration patterns. Our method tracks cells with lineage by using a Generalised Label Multi-Bernoulli (GLMB) filter with objects spawning, and a robust Cardinalised Probability Hypothesis Density (CPHD) to address unknown and time-varying detection probability and clutter rate. The proposed method is capable of quantifying the certainty level of the tracking solutions. The capability of the algorithm on population dynamic inference is demonstrated on a migration sequence of breast cancer cells.

show abstract

Section: Resultsmentioning

confidence: 99%

Biological Cell Tracking And Lineage Inference Via Random Finite Sets

Nguyen

Shim

Kim

2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

View full text Add to dashboard Cite

show abstract

“…The clutter rate is set to 0.05. The cell spawning model is the same as described in Reference [57] with the covariance of the spawning model given as QT=[]40000005000004000000500000π/90 and the smoothing interval set to the entire image sequence. In this application, we set the track pruning threshold of the estimator to 3 time steps.…”

Section: Experimental Resultsmentioning

confidence: 99%

GLMB Tracker with Partial Smoothing

Nguyen

Kim

2019

Sensors

View full text Add to dashboard Cite

In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters.

show abstract

“…Recursive Bayesian approach shows good features in enhancing the state estimation accuracy even in the case of insufficient measurements [2]. The Bayesian framework has become a significant estimation approach with applications including radar or sonar [3], computer vision [4], cell biology [5] [6], autonomous vehicle [7] and sensor network [8]. Taking the numerical complexity of Bayes multi-target filter into consideration, the Probability Hypothesis Density (PHD) [9] filter, Cardinalized PHD (CPHD) [10] filter and multi-target multi-Bernoulli (MeMBer) filter [11] have been developed as approximations.…”

Section: Introductionmentioning

confidence: 99%

“…It outperforms the RFSbased filters mentioned earlier by introducing hypothesis method into the iteration. Implementations such as cell migration [5] [6], multi-sensor framework [8] and joint detection, tracking and classification [14] have been investigated using this state-of-the-art filter. A cheaper method is employed to reduce the complexity in [15] and it is proposed in [16] to adopt trajectories in an interval to describe the multi-target state.…”

Section: Introductionmentioning

confidence: 99%