Automatic fluorescent tag detection in 3D with super‐resolution: application to the analysis of chromosome movement

Thomann, Dominik; Rines, Daniel R.; Sorger, Peter K.; Danuser, Gaudenz

doi:10.1046/j.1365-2818.2002.01066.x

Cited by 187 publications

(179 citation statements)

References 29 publications

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“…In practice, the PSF of a fluorescence microscope is well approximated by a 2D Gaussian [43]. The object appearance in a 2D image is hence modeled as:…”

Section: Observation Modelmentioning

confidence: 99%

PPF: A parallel particle filtering library

Demirel¹,

Smal²,

Niessen³

et al. 2014

IET Conference on Data Fusion &Amp; Target Tracking 2014: Algorithms and Applications

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Abstract. We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributedmemory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with a tool for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency.

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“…In practice, the PSF of a fluorescence microscope is well approximated by a 2D Gaussian [43]. The object appearance in a 2D image is hence modeled as:…”

Section: Observation Modelmentioning

confidence: 99%

PPF: A parallel particle filtering library

Demirel¹,

Smal²,

Niessen³

et al. 2014

IET Conference on Data Fusion &Amp; Target Tracking 2014: Algorithms and Applications

View full text Add to dashboard Cite

show abstract

“…Finally, diffraction results when light passes through an aperture, creating the finite-width Airy disk (Airy, 1835). In our optical PSFs, we assume scattering and diffraction result in Gaussian blurring (Thomann et al, 2002;Tian et al, 2011). Our PSFs assume imaging through a single homogeneous medium; in practice, tissue inhomogeneity and refractive index mismatch can produce additional aberrations in the absorption, scattering, and diffraction domains that we do not model here.…”

Section: General Informationmentioning

confidence: 99%

Spatial Information in Large-Scale Neural Recordings

Cybulski¹,

Glaser²,

Marblestone

et al. 2014

Preprint

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To record from a given neuron, a recording technology must be able to separate the activity of that neuron from the activity of its neighbors. Here, we develop a Fisher information based framework to determine the conditions under which this is feasible for a given technology. This framework combines measurable point spread functions with measurable noise distributions to produce theoretical bounds on the precision with which a recording technology can localize neural activities. If there is sufficient information to uniquely localize neural activities, then a technology will, from an information theoretic perspective, be able to record from these neurons. We (1) describe this framework, and (2) demonstrate its application in model experiments. This method generalizes to many recording devices that resolve objects in space and should be useful in the design of next-generation scalable neural recording systems.

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“…In practice, the PSF of a fluorescence microscope is well approximated by a 2D Gaussian [24,25]. Object appearance in a 2D image is hence modeled as:…”

Section: Likelihood / Appearance Modelmentioning

confidence: 99%

Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Demirel

Smal

Niessen

et al. 2014

IET Conference on Data Fusion &Amp; Target Tracking 2014: Algorithms and Applications

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Abstract. Particle filters are key algorithms for object tracking under non-linear, non-Gaussian dynamics. The high computational cost of particle filters, however, hampers their applicability in cases where the likelihood model is costly to evaluate, or where large numbers of particles are required to represent the posterior. We introduce the piecewise constant sequential importance sampling/resampling (pcSIR) algorithm, which aims at reducing the cost of traditional particle filters by approximating the likelihood with a mixture of uniform distributions over pre-defined cells or bins. The particles in each bin are represented by a dummy particle at the center of mass of the original particle distribution and with a state vector that is the average of the states of all particles in the same bin. The likelihood is only evaluated for the dummy particles, and the resulting weight is identically assigned to all particles in the bin. We derive upper bounds on the approximation error of the so-obtained piecewise constant function representation, and analyze how bin size affects tracking accuracy and runtime. Further, we show numerically that the pcSIR approximation error converges to that of sequential importance sampling/resampling (SIR) as the bin size is decreased. We present a set of numerical experiments from the field of biological image processing and tracking that demonstrate pcSIR's capabilities. Overall, we consider pcSIR a promising candidate for simple, fast particle filtering in generic applications, especially in those with a costly likelihood update step.

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Automatic fluorescent tag detection in 3D with super‐resolution: application to the analysis of chromosome movement

Cited by 187 publications

References 29 publications

PPF: A parallel particle filtering library

PPF: A parallel particle filtering library

Spatial Information in Large-Scale Neural Recordings

Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

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