Evidence map›Paper›PMID 37770830›Full record

ArticleBMC bioinformatics2023

A robust approach to 3D neuron shape representation for quantification and classification.

Jiaxiang Jiang, Michael Goebel, Cezar Borba, William Smith, B S Manjunath

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Connectivity Is All You Need: Inferring Neuronal Types with NTAC.bioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Jiaxiang JiangDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, USA. jjiang00@ucsb.edu.
Michael GoebelDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, USA.
Cezar BorbaThe Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, USA.
William SmithDepartment of Molecular, Cell and Developmental Biology, University of California, Santa Barbara, USA.
B S ManjunathDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, USA. manj@ucsb.edu.

Funding

The connectome and neurobiology of a novel model chordate, Ciona intestinalisR01NS103774 · NINDS · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI MANJUNATH, BANGALORE S, SMITH, WILLIAM · 2017 to 2021
$2.8M
NIH HHS 5R01NS103774-04NINDS NIH HHS R01 NS103774
6 · The paper itself

Abstract

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing "curve" skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

Indexed as

AlgorithmsImaging, Three-DimensionalNeurons3D neuron morphologyClassificationEmbeddingGraphSkeleton meshSub-cellular features

Identifiers

PMID37770830
PMCPMC10537603

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.