Automatic identification of dendritic branches and their orientation

Inbar Dahari, Danny Baranes, Refael Minnes

Research output: Contribution to journalArticlepeer-review


The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology of dendrites is essential for a better understanding of neuronal function. However, the complexity of dendritic trees, both when isolated and especially when located within neuronal networks, has not been completely understood. We developed a new computational tool, SOA (Segmentation and Orientation Analysis), which allows automatic measurement of the orientation of dendritic branches from fluorescence images of 2D neuronal cultures. SOA, written in Python, uses segmentation to distinguish dendritic branches from the image background and accumulates a database on the spatial direction of each branch. The database is then used to calculate morphological parameters such as the directional distribution of dendritic branches in a network and the prevalence of parallel dendritic branch growth. The data obtained can be used to detect structural changes in dendrites in response to neuronal activity and to biological and pharmacological stimuli.

Original languageEnglish
Article numbere62679
JournalJournal of Visualized Experiments
Issue number175
StatePublished - 1 Sep 2021
Externally publishedYes

ASJC Scopus subject areas

  • General Neuroscience
  • General Chemical Engineering
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology


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