Automating the quantitative analysis of 2-D neural dendritic trees

Villa, M.; Amthor, Franklin R.

doi:10.1016/0165-0270(94)00109-t

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…primary, secondary, tertiary, etc. ), number of branches, number of roots, number of bifurcations, number of terminal tips (topological degree) and sequences of branch order (Amthor et al ., 1983; Van Pelt & Verwer, 1986; Uylings et al ., 1989; Montague & Friedlander, 1991; Villa & Amthor, 1995). To study the stratification level of their dendrites, most cells were also three‐dimensionally reconstructed by using Object–Image and Rotator.…”

Section: Methodsmentioning

confidence: 99%

Morphometrical analysis of dendritic arborization in axotomized retinal ganglion cells

Germain

Fernández

Villa

2003

Eur J of Neuroscience

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It has been reported that section of the optic nerve in mammals causes death in >90% of the retinal ganglion cells (RGCs). The cells which survive the section experience an irreparable loss of many of their dendritic segments and a rapid retraction of the dendritic tree. However, some growth cones and abnormal processes have been also reported. Our aim was to make a quantitative study of the morphological changes found in rabbit RGCs after optic nerve section. The morphometrical analysis of the RGCs which survived the axotomy showed an increase in the diameter of the soma and a significant increase in the area of the dendritic field; also, the length of the dendritic segments was significantly longer in axotomized RGCs than in control cells. Terminal dendritic segments (T) and preterminal segments (PT) were both measured in control and axotomized cells; the length ratio of T : PT segments was significantly greater in the axotomized cells than in the controls. We conclude that RGCs which survived the axotomy experienced a significant growth of their terminal dendritic branches.

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Section: Methodsmentioning

confidence: 99%

Morphometrical analysis of dendritic arborization in axotomized retinal ganglion cells

Germain

Fernández

Villa

2003

Eur J of Neuroscience

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“…Morphological differences to elucidate the physiological mechanisms [30] or classify a set of cell populations with different functions such as neurons [31, 32]. …”

Section: Overview Of Contributionsmentioning

confidence: 99%

“…The extracting features as cell area, perimeter, centroid, and the length of major and minor axes for calculating more meaningful parameters such as displacement, protrusiveness, and ellipticity, are used to analyze the dynamic changes of human cancerous glioma cells [35], which can also be used to identify different classed of neurons and relate neural structure (such as total dendritic length and dendritic field area) to function [31]. …”

Section: Tasks and Problemsmentioning

confidence: 99%

Recent Advances in Morphological Cell Image Analysis

Chen

Zhao

et al. 2012

Computational and Mathematical Methods in Medicine

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This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

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“…In instances where geometrically sensible sets of connected voxels are obtained, it then can be useful to subject the data to thinning ("skeletonizing") or watershed procedures, with the aim of obtaining the medial axis (roughly, the local center-point of the data). This can produce (for neurons) a tree structure which is one voxel thick, except at branch points, and that can be automatically traversed in order to extract topological and geometrical properties (see the 1995 work of Mark F. Villa and Franklin R. Amthor 57 for the 2D and Stephen L. Senft 58 for the 3D case). It also is possible in this way to recover an estimate of the diameter at each point, an important variable for understanding the potential metabolic and electrochemical properties of cells.…”

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confidence: 99%

A Brief History of Neuronal Reconstruction

Senft

2011

Neuroinform

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Automating the quantitative analysis of 2-D neural dendritic trees

Cited by 6 publications

References 14 publications

Morphometrical analysis of dendritic arborization in axotomized retinal ganglion cells

Morphometrical analysis of dendritic arborization in axotomized retinal ganglion cells

Recent Advances in Morphological Cell Image Analysis

A Brief History of Neuronal Reconstruction

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