Category Archives: Single Particle Tracking

Grace Hopper Celebration (GHC 2020)

Simultaneous Localization and Parameter Estimation for Single Particle Tracking in Two Dimensional Confined Environments [poster]

I’m so excited to attend the Grace Hopper Celebration for the first time in my life! This event provides a great platform for women in tech to get a lot of innovation and inspiration. Also, it’s a great honor to present one of my research work in the GHC poster session [Poster].  Again, thanks for SE/CISE awarding me academic access to vGHC2020 [More …]. Cheers!

American Control Conference (ACC 2020)

A Time-Varying Approach to Single Particle Tracking with a Nonlinear Observation Model

[Abstract] – Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop a local time-varying estimation algorithm for estimating motion model parameters from the data considering nonlinear observations. Our approach uses several well-known existing tools, namely the Expectation Maximization (EM) algorithm combined with an Unscented Kalman filter (UKF) and an Unscented Rauch-Tung-Striebel smoother (URTSS), and applies them to the time-varying case through a sliding window methodology. Due to the shot noise characteristics of the photon generation process, this model uses a Poisson distribution to capture the measurement noise inherent in imaging. In order to apply our time-varying approach to the UKF, we first need to transform the measurements into a model with additive Gaussian noise. This is carried out using a variance stabilizing transform. Results from simulations show that our approach is successful in tracing time-varying diffusion constants at a range of physically relevant signal levels. We also discuss the initialization for the EM algorithm based on the available data.

Quantitative BioImaging Conference (QBI) 2020.

Quantitative BioImaging Conference (QBI) 2020 Conference at the University of Oxford’s Mathematical Institute, Oxford, UK (January 6-9, 2020). 

[Brief illustration] – Single Particle Tracking (SPT) is a powerful class of tools for acquiring information about the dynamics of biological macromolecules moving inside living cells. In this work, we are interested in comparing algorithms with respect to the performance of localization and motion model estimation at very low signal levels. A standard approach for estimation is to use Gaussian Fitting (GF) to determine the position of an emitter in an image, followed by linking to produce a trajectory. These trajectories are then analyzed by using Mean Square Displacement (MSD) or Maximum Likelihood (ML) to estimate motion parameters, such as diffusion coefficients. However, both MSD and the standard formulation of ML assume a linear measurement model with simple additive Gaussian noise, which begins to fail as image intensity decreases. We have introduced two nonlinear methods that more accurately model camera-based measurements: Sequential Monte Carlo combined with Expectation Maximization (SMC-EM), and Unscented Kalman Filter combined with Expectation Maximization (U-EM).

2019 IEEE 58th Conference on Decision and Control (CDC) at NICE, France.

Simultaneous Localization and Parameter Estimation for Single Particle Tracking via Sigma Points based EM

[Abstract] – Single Particle Tracking (SPT) is a powerful class of tools for analyzing the dynamics of individual biological macromolecules moving inside living cells. The acquired data is typically in the form of a sequence of camera images that are then post-processed to reveal details about the motion. In this work, we develop an algorithm for jointly estimating both particle trajectory and motion model parameters from the data. Our approach uses Expectation-Maximization (EM) combined with an Unscented Kalman filter (UKF) and an Unscented Rauch-Tung-Striebel smoother (URTSS), allowing us to use an accurate, nonlinear model of the observations acquired by the camera. Due to the shot noise characteristics of the photon generation process, this model uses a Poisson distribution to capture the measurement noise inherent in imaging. In order to apply a UKF, we first must transform the measurements into a model with additive Gaussian noise. We consider two approaches, one based on variance stabilizing transformations (where we compare the Anscombe and Freeman-Tukey transforms) and one on a Gaussian approximation to the Poisson distribution. Through simulations, we demonstrate the efficacy of the approach and explore the differences among these measurement transformations.

[Main idea] –  

2019CDC_framework

[Example of measurement]

 

[Reference]

@inproceedings{lin2019simultaneous,
  title={Simultaneous Localization and Parameter Estimation for Single Particle Tracking via Sigma Points based EM},
  author={Lin, Ye and Andersson, Sean B},
  booktitle={2019 IEEE 58th Conference on Decision and Control (CDC)},
  pages={6467--6472},
  year={2019},
  organization={IEEE}
}