Online conversion of reconstructed neural morphologies into standardized SWC format
Abstract Digital reconstructions provide an accurate and reliable way to store, share, model, quantify, and analyze neural morphology. Continuous advances in cellular labeling, tissue processing, microscopic imaging, and automated tracing catalyzed a proliferation of software applications to reconst...
Main Authors: | Ketan Mehta, Bengt Ljungquist, James Ogden, Sumit Nanda, Ruben G. Ascoli, Lydia Ng, Giorgio A. Ascoli |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-11-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-023-42931-x |
Similar Items
-
Automatic identification of scientific publications describing digital reconstructions of neural morphology
by: Patricia Maraver, et al.
Published: (2023-09-01) -
An imaging analysis protocol to trace, quantify, and model multi-signal neuron morphology
by: Sumit Nanda, et al.
Published: (2021-06-01) -
An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology
by: Kayvan Bijari, et al.
Published: (2020-03-01) -
Circuit analysis of the <i>Drosophila</i> brain using connectivity-based neuronal classification reveals organization of key communication pathways
by: Ketan Mehta, et al.
Published: (2023-01-01) -
Module for SWC neuron morphology file validation and correction enabled for high throughput batch processing.
by: Damien M O'Halloran
Published: (2020-01-01)