Table 1.

List of tested segmentation methods and all-in-one segmentation tools and definition of abbreviations

Segmentation step	Abbreviation	Description	Setable parameters	Computational time	Ref.
All in one tools
	aioFasright	Nucleus editor of Farsight toolkit	N/A	4.96 s	[2]
	aioCellX	segmentation, fluorescence quantification, and tracking tool CellX	N/A	10.30 s	[3]
	aioFogbank	single cell segmentation tool FogBank according Chalfoun	N/A	12.00 s	[4]
	aioFastER	fastER - user-friendly tool for ultrafast and robust cell segmentation	N/A	0.42 s	[5]
	aioCellProfiler	tool for cell analysis pipelines including single cell segmentation	N/A	11.8 s	[10]
	aioDMGW	Dry mass-guided watershed method, (Q-PHASE, Tescan)		1.00 s
Reconstruction
	rDIC-Koos	DIC/HMC image reconstruction according Koos	2	36.60 min	[12]
	rDIC-Yin	DIC/HMC image reconstruction according Yin	2	2.10 s	[13]
	rPC-Yin	PC image reconstruction according Yin	4	13.10 min	[14]
	rPC-Tophat	PC image reconstruction according Thirusittampalam and Dewan	1	0.17 s	[15, 16]
Foreground-background segmentation
	sST	simple thresholding	1	<0.01 s
	sOtsu	thresholding using Gaussian distribution	0	<0.01 s	[17]
	sPT	thresholding using Poisson distribution	0	<0.01 s	[2]
	sEGT	empirical gradient threshold	3	0.24 s	[18]
	sPC-Juneau	Feature extraction approach according Juneau	3	0.26 s	[19]
	sPC-Topman	Feature extraction approach according Topman	4	0.35 s	[20]
	sPC-Phantast	Phantast toolbox developed by Jaccard	5	0.35 s	[21]
	sLS-Caselles	Level-set with edge-based method	2	31.40 s	[22]
	sLS-ChanVese	Level-set with region-based method	2	11.10 s	[23]
	sGraphCut	Graph-Cut applied on recosntructed and raw data	2	15.80 s	[24]
	sWekaGraphCut	Graph-Cut applied on probability maps generated by Weka	2	31.80 min**	[24]
	sIlastikGraphCut	Graph-Cut applied on probability maps generated by Ilastik	2	31.52 min**	[24]
	sIlastik	machine learning tool by Sommer	N/A	31.20 min+21 s*	[25].
	sWeka	machine learning tool by Arganda-Carreras	N/A	27.60 min+2.20 min*	[26]
Cell detection (seed-point extraction)
	dLoGm-Peng	multiscale LoG by Peng	4	3.60 s	[27]
	dLoGm-Kong	multiscale LoG by Kong	5	4.20 s	[28]
	dLoGg-Kong	generalized LoG filter by Kong	2	46.40 s	[28]
	dLoGg-Xu	generalized LoG filter by Xu	3	5.10 s	[29]
	dLoGh-Zhang	Hessian analysis of LoG images by Zhang	1	8.90 s	[30]
	dFRST	fast radial-symmetry transform	5	153.10 s	[31]
	dGRST	generalized radial-symmetry transform	5	572.30 s	[32]
	dRV-Qi	radial voting methods by Qi et al.	5	95.00 s	[33]
	dDT-Threshold	distance transform by Thirusittampalam, threshold-generated foreground	4	0.11 s	[15]
	dDT-Weka	distance transform by Thirusittampalam, sWeka-generated foreground	3	0.11 s ‡	[15]
	dMSER	maximally stable extremal region method (MSER)	3	2.10 s	[34]
	dCellDetect	machine learning method based on MSER	1	141.70 s/60.20 s*	[35]
Single cell (instance) segmentation
	MCWS †	Marker-conttrolled watershed	0	1.40 s
	MCWS-dDT †	Marker-conttrolled watershed on DT image	0	1.41 s

For detailed list of optimized parameters see Additional file 1. * computational time for learning based approaches indicated as two values for learning and classification. ** computational time for Weka+Graph cut combination shown as sum time of these methods. ‡ not includes time for Weka probability map creation, † indicate final segmentation step following foreground-background segmentation and seed-point extraction. Number of parameters in “all-in-one” approaches not shown because of the GUI-based nature, similarly, not shown for learning-based approaches, see Methods section for details. Computational time shown for one 1360 ×1024 DIC field of view