Recent advances in holographic 3D particle tracking

Pasquale Memmolo; Lisa Miccio; Melania Paturzo; Giuseppe Di Caprio; Giuseppe Coppola; Paolo A. Netti; Pietro Ferraro

doi:10.1364/AOP.7.000713

Advances in Optics and Photonics
Vol. 7,
Issue 4,
pp. 713-755
(2015)
•https://doi.org/10.1364/AOP.7.000713

Recent advances in holographic 3D particle tracking

Pasquale Memmolo, Lisa Miccio, Melania Paturzo, Giuseppe Di Caprio, Giuseppe Coppola, Paolo A. Netti, and Pietro Ferraro

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Pasquale Memmolo, Lisa Miccio, Melania Paturzo, Giuseppe Di Caprio, Giuseppe Coppola, Paolo A. Netti, and Pietro Ferraro, "Recent advances in holographic 3D particle tracking," Adv. Opt. Photon. 7, 713-755 (2015)

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Abstract

Particle tracking is a fundamental technique for investigating a variety of biophysical processes, from intracellular dynamics to the characterization of cell motility and migration. However, observing three-dimensional (3D) trajectories of particles is in general a challenging task in classical microscopy owing to the limited imaging depth of field of commercial optical microscopes, which represents a serious drawback for the analysis of time-lapse microscopy image data. Therefore, numerous automated particle-tracking approaches have been developed by many research groups around the world. Recently, digital holography (DH) in microscopy has rapidly gained credit as one of the elective techniques for these applications, mainly due to the uniqueness of the DH to provide a posteriori quantitative multiple refocusing capability and phase-contrast imaging. Starting from this paradigm, a huge amount of 3D holographic tracking approaches have been conceived and investigated for applications in various branches of science, including optofluids, microfluidics, biomedical microscopy, cell mechano-trasduction, and cell migration. Since a wider community of readers could be interested in such a review, i.e., not only scientists working in the fields of optics and photonics but also users of particle-tracking tools, it should be very beneficial to provide a complete review of state-of-the-art holographic 3D particle-tracking methods and their applications in bio-microfluidics.

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		Detection	Linking
Method	Authors	Remarks	Principle	Approaches	Remarks	Dim.	Refs.
1	I. F. Sbalzarini, Y. Gong, J. Cardinale	Iterative intensity-weighted centroid calculation	Combinatorial optimization	MF, MT, GC	Greedy hill-climbing optimization with topological constraints.	2D 3D	[32]
2	C. Carthel, S. Coraluppi	Adaptive local-maxima selection	Multiple hypothesis tracking	MF, MT, MM	Motion models are user specified (near-constant position and/or velocity).	2D 3D	[33,34]
3	N. Chenouard, F. de Chaumont, J.-C. Olivo-Marin	Maxima after thresholding two-scale wavelet products	Multiple hypothesis tracking	MF, MT, MM, GC	Motion models are user specified (near-constant position and/or velocity).	2D 3D	[35–37]
4	M. Winter, A. R. Cohen	Adaptive Otsu thresholding	Multitemporal association tracking	MF, MT, GC	Post-tracking refinement of detections.	2D 3D	[38,39]
5	W. J. Godinez, K. Rohr	Either thresholding + centroid or maxima + Gaussian fitting	Kalman filtering + probabilistic data association	MF, MM	Interacting multiple models using motion models as specified.	2D 3D	[29,4]
6	Y. Kalaidzidis	Lorentzian function fitting to structures above noise level	Dynamic programming	MF, GC	Track assignment by the weighted sum of multiple features.	2D	[41]
7	L. Liang, J. Duncan, H. Shen, Y. Xu	Gaussian mixture model fitting	Multiple hypothesis tracking	MF, MM	Interacting multiple models with forward and backward linking.	2D	[42]
8	K. E. G. Magnusson, J. Jaldén, H. M. Blau	Watershed-based clump splitting and parabola fitting	Viterbi algorithm on state-space representation	MF, MT	Brownian motion is assumed in all cases.	2D 3D	[43,44]
9	P. Paul-Gilloteaux	Either maxima with pixel precision (2D) or thresholding + Gaussian fitting (3D)	Nearest neighbor + global optimization	MF, MT, GC	Global optimization of associations using simulated annealing.	2D 3D	[45,46]
10	P. Roudot, C. Kervrann, F. Waharte	Histogram-based thresholding and Gaussian fitting	Gaussian template matching	–	Only local and per-trajectory particle linking.	2D	[47–49]
11	I. Smal, E. Meijering	Gaussian fitting (round particles) or centroid calculation (elongated particles)	Sequential multiframe assignment	MF, MT, MM, GC	Global linking cost minimization.	2D	[35,50,51]
12	J.-Y. Tinevez, S. L. Shorte	Parabolic fitting to localized maxima	Linear assignment problem	MT, GC	Two-step approach (frame-to-frame and segment linking).	2D 3D	[52,53]
13	J. Willemse, K. Celler, G. P. van Wezel	Watershed-based clump splitting	Nearest neighbor	MM, GC	Allows merging and splitting of particles and uses a linear motion model.	2D 3D	[54,55]
14	H.-W. Dan, Y.-S. Tsai	Morphological opening-based clump splitting	Nearest neighbor + Kalman filtering	MM	Essentially a 2D method keeping track of maximum intensity in $z$ .	2D 3D	[56,57]

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Advances in Optics and Photonics