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SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline.

Liya Ding1, Xuan Zhao1, Shuxia Guo1

  • 1Institute for Brain and Intelligence, Southeast University, Nanjing, China.

Frontiers in Neuroinformatics
|June 30, 2023
PubMed
Summary

SNAP, a novel pipeline, refines neuron morphology reconstruction by reducing errors and splitting entangled neurons. This improves the accuracy and usability of automated neuron analysis for cell-type definition.

Keywords:
bioinformaticsdendrite tracingimage processingneuron morphology reconstructionpost-processing

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Bioinformatics

Background:

  • Neuron morphology analysis is crucial for defining neuron cell types.
  • Automated morphology reconstruction faces challenges with noise and entanglement, limiting usability.
  • Erroneous reconstructions hinder high-throughput analysis workflows.

Purpose of the Study:

  • To introduce SNAP, a structure-based pipeline for pruning neuron morphology reconstructions.
  • To enhance the usability of automated reconstruction results by reducing errors and splitting entangled neurons.
  • To improve the accuracy of neuron morphology analysis.

Main Methods:

  • SNAP employs a structure-based approach for neuron morphology reconstruction pruning.
  • It incorporates statistical structure information to detect and remove erroneous extra segments.
  • The pipeline performs pruning and multiple dendrite splitting to address noise and entanglement.

Main Results:

  • SNAP effectively prunes erroneous reconstructions with high precision and recall.
  • The pipeline demonstrates strong performance in splitting multiple entangled neurons.
  • Experimental results validate SNAP's capability as a post-processing tool.

Conclusions:

  • SNAP significantly improves the usability of automated neuron morphology reconstruction.
  • This pipeline facilitates more accurate and reliable neuron morphology analysis.
  • SNAP is an effective tool for post-processing reconstruction data.