Table 1.

Summary of non-classification approaches to dendritic spines shapes analysis.

No	Year	Reference	Sample	Microscopy	Spines form representation (Figure 3)	Preprocessing and feature extraction approach	Number of features	Software (+ provided,− not provided)	Morphological features analysis approach	Software availability
1	2016	Ghani et al. (2017)	7–10 day old mouse brain slices neurons (region not specified)	Two-photon	2D projection from image series	Histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, intensity profile based features or their combination	Varying, from 12 for morphological to 346 for DNSM features	Custom (−)	X-means clustering, number of clusters selected automatically using the Bayesian information criterion (BIC; =4)	NA
2	2016	Bokota et al. (2016)	19–21 days in vitro hippocampal neurons	Confocal	2D projection from image series	The most often used morphological features according to literature data	11 (reduced to 6)	Custom (−)	C-means clustering (=10), average-linkage hierarchical clustering (=10), data dimensionality reduction by 2D principal component analysis (PCA)	UR
3	2018	Luengo-Sanchez et al. (2018)	layer III pyramidal neurons of the human cingulate cortex	Confocal	3D triangular surface mesh Multiresolution Reeb graph	Surface of a spine is modeled by 7 segments, which are presented as linked to each other ellipses. From 54 parameters used to describe dendritic spines, 36 reflect ellipses geometry and position and 18 describe more complex features, such as spine growth direction	54	Imaris for segmentation ($), custom software for feature extraction (+)	Clustering by probabilistic model with Gaussian finite mixtures, number of clusters selected automatically using the Bayesian information criterion (BIC; =6)	FA
4	2019	Kashiwagi et al. (2019)	18–22 days in vitro hippocampal neurons	SIM	3D triangular surface mesh	Segmentation of spines by multilevel thresholding based on Otsu’s method following geodesic active contour, combination of morphological features and high geometric features	10 (reduced to 5)	Custom (+)	Division into mushroom and non-mushroom spines using SVM classifier, mapping the trajectories of individual spines shape transitions in the feature space, data dimensionality reduction by 3D principal component analysis (PCA)	FA
5	2019	Choi et al. (2019)	18–22 days in vitro hippocampal neurons	SIM	3D triangular surface mesh	Processing as in No4 with addition of 5 more features reflecting spines head and neck size	10	DXplorer (−)	K-means clustering, coordinate plot, radar plot and 2D scatter plot with t-Distributed Stochastic Neighbor Embedding	NA
6	2019	Driscoll et al. (2019)	CLARITY-cleared mouse brain neurons (region not specified)	LSM		Machine-learning based supervised spines detection	n/d	u-shape3D software (+)	Unsupervised hierarchical clustering (=9), data dimensionality deduction by 2D principal component analysis (PCA)	NA

Software availability—NA, not available; UR, available upon request; FA, freely available.