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Journal ArticleDOI

Metadata matters: access to image data in the real world

TL;DR: An open standard format for multidimensional microscopy image data is described and it is called on the community to use open image data standards and to insist that all imaging platforms support these file formats.
Abstract: Data sharing is important in the biological sciences to prevent duplication of effort, to promote scientific integrity, and to facilitate and disseminate scientific discovery. Sharing requires centralized repositories, and submission to and utility of these resources require common data formats. This is particularly challenging for multidimensional microscopy image data, which are acquired from a variety of platforms with a myriad of proprietary file formats (PFFs). In this paper, we describe an open standard format that we have developed for microscopy image data. We call on the community to use open image data standards and to insist that all imaging platforms support these file formats. This will build the foundation for an open image data repository.
Citations
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Journal ArticleDOI
TL;DR: The origins, challenges and solutions of NIH Image and ImageJ software are discussed, and how their history can serve to advise and inform other software projects.
Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.

44,587 citations

Journal ArticleDOI
TL;DR: ImageJ2 as mentioned in this paper is the next generation of ImageJ, which provides a host of new functionality and separates concerns, fully decoupling the data model from the user interface.
Abstract: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

4,093 citations

Journal ArticleDOI
TL;DR: QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images, making it suitable for a wide range of additional image analysis applications across biomedical research.
Abstract: QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

2,838 citations

Journal ArticleDOI
15 Feb 2017-Methods
TL;DR: TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment and is validated for quantitative lifetime analysis of clathrin-mediated endocytosis in plant cells.

2,356 citations


Cites methods from "Metadata matters: access to image d..."

  • ...By being an open source Fiji plugin that uses modular libraries such as Bio-formats [57], SciJava [18], ImgLib2 [58], etc....

    [...]

Posted Content
TL;DR: The entire ImageJ codebase was rewrote, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements.
Abstract: ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.

2,156 citations


Cites background from "Metadata matters: access to image d..."

  • ...includes a wrapping of the Bio-Formats library [51], which enables a wide variety of supported images throughout all ImageJ operations....

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  • ...Bio-Formats [51] SCIFIO-Bio-Formats [104] SCIFIO-OME-XML [105]...

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References
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Journal ArticleDOI
Ed S. Lein1, Michael Hawrylycz1, Nancy Ao2, Mikael Ayres1, Amy Bensinger1, Amy Bernard1, Andrew F. Boe1, Mark S. Boguski3, Mark S. Boguski1, Kevin S. Brockway1, Emi J. Byrnes1, Lin Chen1, Li Chen2, Tsuey-Ming Chen2, Mei Chi Chin1, Jimmy Chong1, Brian E. Crook1, Aneta Czaplinska2, Chinh Dang1, Suvro Datta1, Nick Dee1, Aimee L. Desaki1, Tsega Desta1, Ellen Diep1, Tim A. Dolbeare1, Matthew J. Donelan1, Hong-Wei Dong1, Jennifer G. Dougherty1, Ben J. Duncan1, Amanda Ebbert1, Gregor Eichele4, Lili K. Estin1, Casey Faber1, Benjamin A.C. Facer1, Rick Fields2, Shanna R. Fischer1, Tim P. Fliss1, Cliff Frensley1, Sabrina N. Gates1, Katie J. Glattfelder1, Kevin R. Halverson1, Matthew R. Hart1, John G. Hohmann1, Maureen P. Howell1, Darren P. Jeung1, Rebecca A. Johnson1, Patrick T. Karr1, Reena Kawal1, Jolene Kidney1, Rachel H. Knapik1, Chihchau L. Kuan1, James H. Lake1, Annabel R. Laramee1, Kirk D. Larsen1, Christopher Lau1, Tracy Lemon1, Agnes J. Liang2, Ying Liu2, Lon T. Luong1, Jesse Michaels1, Judith J. Morgan1, Rebecca J. Morgan1, Marty Mortrud1, Nerick Mosqueda1, Lydia Ng1, Randy Ng1, Geralyn J. Orta1, Caroline C. Overly1, Tu H. Pak1, Sheana Parry1, Sayan Dev Pathak1, Owen C. Pearson1, Ralph B. Puchalski1, Zackery L. Riley1, Hannah R. Rockett1, Stephen A. Rowland1, Joshua J. Royall1, Marcos J. Ruiz2, Nadia R. Sarno1, Katherine Schaffnit1, Nadiya V. Shapovalova1, Taz Sivisay1, Clifford R. Slaughterbeck1, Simon Smith1, Kimberly A. Smith1, Bryan I. Smith1, Andy J. Sodt1, Nick N. Stewart1, Kenda-Ruth Stumpf1, Susan M. Sunkin1, Madhavi Sutram1, Angelene Tam2, Carey D. Teemer1, Christina Thaller2, Carol L. Thompson1, Lee R. Varnam1, Axel Visel5, Axel Visel4, Ray M. Whitlock1, Paul Wohnoutka1, Crissa K. Wolkey1, Victoria Y. Wong1, Matthew J.A. Wood2, Murat B. Yaylaoglu2, Rob Young1, Brian L. Youngstrom1, Xu Feng Yuan1, Bin Zhang2, Theresa A. Zwingman1, Allan R. Jones1 
11 Jan 2007-Nature
TL;DR: An anatomically comprehensive digital atlas containing the expression patterns of ∼20,000 genes in the adult mouse brain is described, providing an open, primary data resource for a wide variety of further studies concerning brain organization and function.
Abstract: Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of approximately 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.

4,944 citations

Journal ArticleDOI
05 Oct 2007-Cell
TL;DR: A high-resolution fluorescent in situ hybridization procedure was developed and employed to comprehensively evaluate mRNA localization dynamics during early Drosophila embryogenesis, indicating major roles for mRNA localization in nucleating localized cellular machineries.

942 citations

Journal ArticleDOI
24 Mar 2005-Nature
TL;DR: This work used RNA-mediated interference to target 98% of all genes predicted in the C. elegans genome in combination with differential interference contrast time-lapse microscopy and developed a phenotypic profiling system, which shows high correlation with cellular processes and biochemical pathways, thus enabling to predict new functions for previously uncharacterized genes.
Abstract: A key challenge of functional genomics today is to generate well-annotated data sets that can be interpreted across different platforms and technologies. Large-scale functional genomics data often fail to connect to standard experimental approaches of gene characterization in individual laboratories. Furthermore, a lack of universal annotation standards for phenotypic data sets makes it difficult to compare different screening approaches. Here we address this problem in a screen designed to identify all genes required for the first two rounds of cell division in the Caenorhabditis elegans embryo. We used RNA-mediated interference to target 98% of all genes predicted in the C. elegans genome in combination with differential interference contrast time-lapse microscopy. Through systematic annotation of the resulting movies, we developed a phenotypic profiling system, which shows high correlation with cellular processes and biochemical pathways, thus enabling us to predict new functions for previously uncharacterized genes.

921 citations


"Metadata matters: access to image d..." refers background in this paper

  • ...…2010 PhenoBank Database Genome-wide C. elegans screen for functional roles in early embryonic mitotic divisions http://worm.mpi-cbg.de/phenobank2/ Sönnichsen et al., 2005 American Society for Cell Biology Image & Video Librarya Scientific image and video archive…...

    [...]

Journal ArticleDOI
01 Apr 2010-Nature
TL;DR: This study carried out a genome-wide phenotypic profiling of each of the ∼21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival.
Abstract: Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the approximately 21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.

812 citations


"Metadata matters: access to image d..." refers background in this paper

  • ...…Riffle et al., 2005 MitoCheck Genome-wide siRNA screen of mitotic phenotypes in HeLa cells http://www.mitocheck.org Neumann et al., 2010 PhenoBank Database Genome-wide C. elegans screen for functional roles in early embryonic mitotic…...

    [...]

Journal ArticleDOI
TL;DR: The Zebrafish Information Network (ZFIN) provides an integrated representation of mutants, genes, genetic markers, mapping panels, publications and community resources such as meeting announcements and contact information.
Abstract: The Zebrafish Information Network (ZFIN) is a web based community resource that serves as a centralized location for the curation and integration of zebrafish genetic, genomic and developmental data. ZFIN is publicly accessible at http://zfin.org. ZFIN provides an integrated representation of mutants, genes, genetic markers, mapping panels, publications and community contact data. Recent enhancements to ZFIN include: (i) an anatomical dictionary that provides a controlled vocabulary of anatomical terms, grouped by developmental stages, that may be used to annotate and query gene expression data; (ii) gene expression data; (iii) expanded support for genome sequence; (iv) gene annotation using the standardized vocabulary of Gene Ontology (GO) terms that can be used to elucidate relationships between gene products in zebrafish and other organisms; and (v) collaborations with other databases (NCBI, Sanger Institute and SWISS-PROT) to provide standardization and interconnections based on shared curation.

369 citations

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