Evidence map›Paper›PMID 21547564›Full record

ArticleNeuroinformatics2011

Automated reconstruction of neuronal morphology based on local geometrical and global structural models.

Ting Zhao, Jun Xie, Fernando Amat, Nathan Clack, Parvez Ahammad, Hanchuan Peng, Fuhui Long, Eugene Myers

Abstract read
In one paragraph

Article in Neuroinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing 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

53 citing papers in PubMed.

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  16. Visualization of Neuronal Structures in Wide-Field Microscopy Brain Images.IEEE transactions on visualization and computer graphics · 2018
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ting ZhaoQiushi Academy for Advanced Studies, Zhejiang University, 38 ZheDa Road, Hangzhou 310027, China. tingzhao@gmail.com
Jun Xie
Fernando Amat
Nathan Clack
Parvez Ahammad
Hanchuan Peng
Fuhui Long
Eugene Myers

Funding

Howard Hughes Medical Institute
6 · The paper itself

Abstract

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets.

Indexed as

AlgorithmsModels, NeurologicalAnimalsComputer SimulationHumansImage Processing, Computer-AssistedImaging, Three-DimensionalNeuronsSoftwareSoftware DesignSoftware Validation

Identifiers

PMID21547564
PMCPMC3104133

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.