Evidence map›Paper›PMID 29875639›Full record

ArticleFrontiers in neuroanatomy2018

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images.

Gherardo Varando, Ruth Benavides-Piccione, Alberto Muñoz, Asta Kastanauskaite, Concha Bielza, Pedro Larrañaga, Javier DeFelipe

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroanatomy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.0field-weighted citation impact, top 17% of its field
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

5 citing papers in PubMed, 5 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Gherardo VarandoComputational Intelligence Group, Department of Artificial Intelligence, Universidad Politécnica de Madrid, Madrid, Spain.
Ruth Benavides-PiccioneDepartamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Madrid, Spain.
Alberto MuñozDepartamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Madrid, Spain.
Asta KastanauskaiteDepartamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Madrid, Spain.
Concha BielzaComputational Intelligence Group, Department of Artificial Intelligence, Universidad Politécnica de Madrid, Madrid, Spain.
Pedro LarrañagaComputational Intelligence Group, Department of Artificial Intelligence, Universidad Politécnica de Madrid, Madrid, Spain.
Javier DeFelipeDepartamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Madrid, Spain.
Universidad Politécnica de Madrid · ESInstituto Cajal · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.

Indexed as

brain atlasfluorescent imageImageJobject detectionpuncta segmentationsegmentationvgatvglut1

Identifiers

PMID29875639
PMCPMC5974206
OpenAlexW2799816053

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.