Evidence map›Paper›PMID 33166005›Full record

ArticleProtein science : a publication of the Protein Society2021

The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis.

Alexandra B Schroeder, Ellen T A Dobson, Curtis T Rueden, Pavel Tomancak, Florian Jug, Kevin W Eliceiri

Open access · hybridAbstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 172 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
172citing papers in PubMed, 1 pooled it
24.4field-weighted citation impact, top 1% 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

172 citing papers in PubMed, 1 synthesis or guideline pooled it, 345 citations in OpenAlex.

  1. Pooled it
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112 more citing papers are in PubMed but not listed here.

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 at 3 institutions in 4 countries.

Alexandra B SchroederLaboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin at Madison, Madison, Wisconsin, USA.
Ellen T A DobsonLaboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin at Madison, Madison, Wisconsin, USA.
Curtis T RuedenLaboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin at Madison, Madison, Wisconsin, USA.
Pavel TomancakMax Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
Florian JugMax Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
Kevin W EliceiriLaboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin at Madison, Madison, Wisconsin, USA.ORCID 0000-0001-8678-670X
University of Wisconsin–Madison · USHuman Technopole · ITVSB - Technical University of Ostrava · CZ

Funding

Center for Open Bioimage AnalysisP41GM135019 · NIGMS · BROAD INSTITUTE, INC. · PI CARPENTER, ANNE E. · 2020 to 2024
$6.9M
NIGMS NIH HHS P41 GM135019NIGMS NIH HHS P41-GM135019
6 · The paper itself

Abstract

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open-source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user-centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedSoftwareFijiimage analysisImageJimagingmicroscopyopen source software

Identifiers

PMID33166005
PMCPMC7737784
OpenAlexW3105310231

What OpenQuestion holds

Textmetadata
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.