Evidence map›Paper›PMID 42129638›Full record

ArticleBMC microbiology2026

An automated workflow for fungal cell counting, cell size and data analysis: enhancing throughput and accuracy with the cell analysis and counting tool using ilastik software (caactus).

Jakob Scheler, Dominik Kutra, Vincent Beliveau, Johannes Seiler, Samuel Pröll, Hannah Sittel, Alina Nowak-Rainer, Hannah Holzer, Cornelia Lass-Flörl, Ulrike Binder

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In one paragraph

Article in BMC microbiology, 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
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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

10 authors.

Jakob SchelerInstitute of Hygiene and Medical Microbiology, Medical University of Innsbruck, Schöpfstraße 41, Innsbruck, 6020, Tyrol, Austria. jakob.scheler@i-med.ac.at.
Dominik KutraCell biology and biophysics, European Molecular Biology Laboratory (EMBL), Meyerhofstraße 1, Heidelberg, 69117, Baden-Württemberg, Germany.
Vincent BeliveauInstitute for Human Genetics, Medical University of Innsbruck, Schöpfstraße 24, Innsbruck, 6020, Tyrol, Austria.
Johannes SeilerDepartment of Statistics, University of Innsbruck, Universitätsstraße 15, Innsbruck, 6020, Tyrol, Austria.
Samuel PröllDigital Cadiology Lab, University Clinic of Internal Medicine III, Medical University of Innsbruck, Anichstraße 35, Innsbruck, 6020, Tyrol, Austria.
Hannah SittelInstitute of Hygiene and Medical Microbiology, Medical University of Innsbruck, Schöpfstraße 41, Innsbruck, 6020, Tyrol, Austria.
Alina Nowak-RainerInstitute of Hygiene and Medical Microbiology, Medical University of Innsbruck, Schöpfstraße 41, Innsbruck, 6020, Tyrol, Austria.
Hannah HolzerInstitute of Hygiene and Medical Microbiology, Medical University of Innsbruck, Schöpfstraße 41, Innsbruck, 6020, Tyrol, Austria.
Cornelia Lass-FlörlInstitute of Hygiene and Medical Microbiology, Medical University of Innsbruck, Schöpfstraße 41, Innsbruck, 6020, Tyrol, Austria.
Ulrike BinderInstitute of Hygiene and Medical Microbiology, Medical University of Innsbruck, Schöpfstraße 41, Innsbruck, 6020, Tyrol, Austria. ulrike.binder@i-med.ac.at.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe development of filamentous fungal cells from spore to mature mycelium is influenced by a myriad of environmental conditions. Traditional methods for quantifying various fungal cell morphologies in liquid media via microscopy are labor-intensive and prone to user-dependent variability.

methodsThis study addresses these challenges by introducing a streamlined workflow for fungal cell analysis, utilizing ilastik-based image segmentation and Python for fast data preparation and analysis. The goal is to offer a user-friendly method for counting and analyzing different fungal cell morphologies and their sizes that is free of charge and accessible to users with minimal programming experience.

resultsThe presented workflow significantly reduces the time required to determine cell developmental stages from microscopy images. It enables rapid results, as compared to manual counting. It also minimizes inter-user variability, enhancing consistency across analyses. It provides a high-throughput, automated solution for fungal cell analysis allowing users to process hundreds of images (n = 48-438) for analysis in a short time period (9-56 min).

conclusionThis approach shows great potential for accelerating research and improving data reliability in biological studies. Furthermore, it provides laboratories in resource limited settings with a free to use solution.

Indexed as

FungiImage Processing, Computer-AssistedSoftwareData AnalyticsMicroscopySpores, FungalWorkflowAntifungal susceptibility testingFungiIlastikMachine-learningMolds

Identifiers

PMID42129638
PMCPMC13340366

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.