Evidence map›Paper›PMID 41617476›Full record

ArticleeNeuro2026

Cell Analyser in Batch for Neurite (CABaNe), an Automated, High-Throughput ImageJ Macro for Cell and Neurite Analysis.

Nathan Thibieroz, Fabrice Cordelières, Paul Machillot, Akshita Singh, Lisa Marchadier, Catherine Picart, Elisa Migliorini

Abstract read
In one paragraph

Article in eNeuro, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Nathan ThibierozUniv Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR BRM 5000, Grenoble 38000, France.
Fabrice CordelièresCNRS UMS 3420, Bordeaux Imaging Center, Bordeaux 33000, France.
Paul MachillotUniv Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR BRM 5000, Grenoble 38000, France.ORCID 0000-0002-5640-929X
Akshita SinghUniv Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR BRM 5000, Grenoble 38000, France.
Lisa MarchadierUniv Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR BRM 5000, Grenoble 38000, France.
Catherine PicartUniv Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR BRM 5000, Grenoble 38000, France lisa.migliorini@cea.fr catherine.picart@inserm.fr.
Elisa MiglioriniUniv Grenoble Alpes, CEA, INSERM U1292 Biosanté, CNRS EMR BRM 5000, Grenoble 38000, France lisa.migliorini@cea.fr catherine.picart@inserm.fr.ORCID 0000-0003-3264-2027

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Measuring neurite length is crucial in neurobiology because it provides valuable insights into the growth, development, and function of neurons. In particular, neurite length is fundamental to study neuronal development and differentiation, neurons responses to drugs, neurodegenerative diseases and neuronal plasticity. Surprisingly, there is currently a lack of tools for high-throughput neurite analysis. In this article, we present CABaNe, as an open-source, high-throughput, rule-based ImageJ macro for cell analysis, including their neurite length. This macro possesses a graphical interface, metadata production, as well as verification means before and after analysis. Rule-based and machine learning-based programming have been tested for cell identification. Cell tested were N2A, a mouse neuroblastoma cell line. After testing, we had better precision and adaptability using rule-based cell identification. We challenged CABaNe with currently used techniques, which are manual or assisted. When tested on a small sample, CABaNe analyzed the dataset of interest much faster than manual measurements, while maintaining or increasing precision. When tested on a large dataset, comparing different conditions, we successfully highlighted differences between conditions, in a fully automated manner. Therefore, CABaNe is viable as a high-throughput option for cell analysis, for neurite length and other parameters. It is a base of code that can be used for other analysis or to train deep learning models. In the future, we expect this tool to be widely used in both basic and applied neurobiology research.

Indexed as

Image Processing, Computer-AssistedNeuritesSoftwareAnimalsCell Line, TumorHigh-Throughput Screening AssaysMachine LearningMicehigh-throughput; ImageJ macro; morphologyneurites lengthneuroblastomarule-based programing

Identifiers

PMID41617476
PMCPMC12931970

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LicenceCC BY
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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.