Evidence map›Paper›PMID 23829479›Full record

ArticleDiagnostic pathology2013

Analyzing huge pathology images with open source software.

Christophe Deroulers, David Ameisen, Mathilde Badoual, Chloé Gerin, Alexandre Granier, Marc Lartaud

Abstract readComparative Study
In one paragraph

Article in Diagnostic pathology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing 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

55 citing papers in PubMed.

  1. Article
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  7. High-performance Data Management for Whole Slide Image Analysis in Digital Pathology.Proceedings of SPIE--the International Society for Optical Engineering · 2024
    Article
  8. Article
  9. Reactivation of Early-Life Stress-Sensitive Neuronal Ensembles Contributes to Lifelong Stress Hypersensitivity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
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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

6 authors.

Christophe DeroulersUniv Paris Diderot, Laboratoire IMNC, UMR 8165 CNRS, Univ Paris-Sud, Orsay F-91405, France. deroulers@imnc.in2p3.fr
David Ameisen
Mathilde Badoual
Chloé Gerin
Alexandre Granier
Marc Lartaud

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer's memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and the others require expensive hardware while still being prohibitively slow.

resultsWe have developed several cross-platform open source software tools to overcome these limitations. The NDPITools provide a way to transform microscopy images initially in the loosely supported NDPI format into one or several standard TIFF files, and to create mosaics (division of huge images into small ones, with or without overlap) in various TIFF and JPEG formats. They can be driven through ImageJ plugins. The LargeTIFFTools achieve similar functionality for huge TIFF images which do not fit into RAM. We test the performance of these tools on several digital slides and compare them, when applicable, to standard software. A statistical study of the cells in a tissue sample from an oligodendroglioma was performed on an average laptop computer to demonstrate the efficiency of the tools.

conclusionsOur open source software enables dealing with huge images with standard software on average computers. They are cross-platform, independent of proprietary libraries and very modular, allowing them to be used in other open source projects. They have excellent performance in terms of execution speed and RAM requirements. They open promising perspectives both to the clinician who wants to study a single slide and to the research team or data centre who do image analysis of many slides on a computer cluster. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here:http://www.diagnosticpathology.diagnomx.eu/vs/5955513929846272.

Indexed as

MicroscopySoftwareHumansImage Interpretation, Computer-AssistedPathologyPredictive Value of TestsUser-Computer Interface

Identifiers

PMID23829479
PMCPMC3706353

What OpenQuestion holds

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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.