Table 1. Tracking methods in bio-imaging.

Algorithm	Type	Detection	Linking	Gap closing	Pros	Cons	Freely available	Ref.
Sage et al.	Global	None	Energy minimization	Yes	Global.	Designed for one or few sparse particles	ImageJ plugin	[53]
Bonneau et al.	Global	None	Energy minimization	Yes	Global. Robust gap closing with minimal-path algorithm	Designed for few sparse particles. High computational load.	No	[54]
NeRVE	Detect & Mapping	Watershed Segmentation	Point-set registration & clustering–Animal deformation estimated with elastic transformations	Yes	Robust to dense packing of particles. Handles non-linear deformations. Time-independent (i.e. robust even in long time-lapse sequences)	High computational load. Not robust to many missing detections and long gaps in highly deforming environments.	Matlab GUI	[10]
fDLC	Detect & Mapping	Watershed Segmentation	Point-set registration to reference set of positions–learning of animal deformation	Yes	Robust to dense packing of particles. Handles non-linear deformations. Time-independent	Not robust to many missing detections and long gaps in highly deforming environments.	Python (Github repository)	[16]
CRF_ID	Detect & Mapping	Gaussian mixture model fitting	Point-set registration to reference & temporally-nearby frames–Use of graphical model (neighbors) to predict identities	Yes	Robust to dense packing of particles. Handles non-linear deformations. Time-independent	High computational load. Not robust to many missing detections and long gaps in highly deforming environments.	Matlab (Github repository)	[22]
Mosaic	Detect & Link	Gaussian Convolution & Thresholding	Global distance minimization	Yes	Fast. Accounts for spot intensity and size in distance computation.	Gap closing does not handle large motion. Not robust in very cluttered conditions.	Particle Tracker plugin (ImageJ)	[32]
TrackMate	Detect & Link	Wavelet transformation or Gaussian convolution & thresholding	Global distance minimization	Yes	Fast. Handles split & merge events.	Gap closing does not handle large, non-linear motion. Many user-defined parameters	TrackMate plugin (ImageJ)	[33]
eMHT	Detect & Link	Wavelet transformation & thresholding	Probabilistic (Multiple Hypothesis)	Yes	Robust in cluttered environment. Few user-defined parameters	Slower than global distance minimization. Cannot close large gaps (> ~5 frames) due to computational load	Spot Tracking plugin (Icy)	[30]
MAP-4D-DAE	Detect & Link	Not specified	Probabilistic (Multiple Hypothesis) + Autoencoding for particle motion modeling	Yes	Robust in cluttered environment. Few user-defined parameters. Handles non-linear deformations	Slower than global distance minimization. Cannot close large gaps (> ~5 frames) due to computational load	No	[10]