Table 1.

Participating teams and tracking methods

Method	Authors	Detection			Linking			Dim.	Refs.
		Prefilter	Approaches	Remarks	Principle	Approaches	Remarks
1	I.F. Sbalzarini Y. Gong J. Cardinale	–	M, C	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	Disk	M, T	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	Wavelets	M, T	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	35,36,37
4	M. Winter A.R. Cohen	Gaussian, median and morphology	M, T, C	Adaptive Otsu thresholding	Multitemporal association tracking	MF, MT, GC	Post-tracking refinement of detections	2D & 3D	38,39
5	W.J. Godinez K. Rohr	Laplacian of Gaussian or Gaussian fitting	M, T, F, C	Either thresholding + centroid or maxima + Gaussian fitting	Kalman filtering + probabilistic data association	MF, MM	Interacting multiple models using motion models as specified	2D & 3D	29,40
6	Y. Kalaidzidis	Windowed floating mean background subtraction	T, F	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	Laplacian of Gaussian	M, T, F	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	Deconvolution	M, T, F	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	Laplacian of Gaussian or Gaussian filtering	M, T, F	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	Structure tensor	T, F	Histogram-based thresholding and Gaussian fitting	Gaussian template matching	–	Only local and per-trajectory particle linking	2D	47,48,49
11	I. Smal E. Meijering	Wavelets	M, F, C	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	Difference of Gaussian	M, T, F	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	Gaussian and top hat	T, C	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	Gaussian, Wiener and top hat	T, C	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

See Supplementary Note 1 for further details on methods 1–14. Dim, dimensionality. Detection approaches: M, maxima detection; T, thresholding; F, fitting; C, centroid estimation. Linking approaches: MF, multiframe; MT, multitrack; MM, motion models; GC, gap closing.