Curtis Rueden | University of Wisconsin-Madison (original) (raw)

Papers by Curtis Rueden

Research paper thumbnail of VisBio: a Flexible Open-Source Visualization Package for Multidimensional Image Data

Microscopy Today

Over the past few years there has been a dramatic improvement in microscopy acquisition technique... more Over the past few years there has been a dramatic improvement in microscopy acquisition techniques, in effective imaging modalities as well as raw hardware performance. As the microscopist's available tools become more sophisticated and diverse—e.g., time-lapse, Z sectioning, multispectra, lifetime, nth harmonic, polarization, and many combinations thereof—we face a corresponding increase in complexity in the software for understanding and interpreting the resultant data. With lifetime imaging, for example, it is overwhelming to study the raw numbers; instead, an exponential curve-fitting algorithm must be applied to extract meaningful lifetime values from the mass of photon counts recorded by the instrument.

Research paper thumbnail of Scientific Community Image Forum: A discussion forum for scientific image software

PLOS Biology

Forums and email lists play a major role in assisting scientists in using software. Previously, e... more Forums and email lists play a major role in assisting scientists in using software. Previously, each open-source bioimaging software package had its own distinct forum or email list. Although each provided access to experts from various software teams, this fragmentation resulted in many scientists not knowing where to begin with their projects. Thus, the scientific imaging community lacked a central platform where solutions could be discussed in an open, software-independent manner. In response, we introduce the Scientific Community Image Forum, where users can pose software-related questions about digital image analysis, acquisition, and data management.

Research paper thumbnail of Assessing microscope image focus quality with deep learning

BMC bioinformatics, Jan 15, 2018

Large image datasets acquired on automated microscopes typically have some fraction of low qualit... more Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which we...

Research paper thumbnail of FunImageJ: a Lisp framework for scientific image processing

Bioinformatics (Oxford, England), Jan 2, 2017

FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecos... more FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecosystem. The framework provides a natural functional-style for programming, while accounting for the performance requirements necessary in big data processing commonly encountered in biological image analysis. Freely available plugin to Fiji (http://fiji.sc/#download). Installation and use instructions available at (http://imagej.net/FunImageJ). kharrington@uidaho.edu. Supplementary data are available at Bioinformatics online.

Research paper thumbnail of The ImageJ Ecosystem: An Open and Extensible Platform for Biomedical Image Analysis

Microscopy and Microanalysis

Research paper thumbnail of ImageJ: Image Analysis Interoperability for the Next Generation of Biological Image Data

Microscopy and Microanalysis

Research paper thumbnail of DIMACS Workshop on Visualization and Data Mining

Research paper thumbnail of The VisAD Java Class Library for Scientific Data and Visualization

ABSTRACT VisAD is a Java class library for interactive and collaborative visualization and analys... more ABSTRACT VisAD is a Java class library for interactive and collaborative visualization and analysis of numerical data. It is designed to support distributed computing and data sharing on the Internet through the use of distributed objects and a very general numerical data model. The data model integrates metadata for data organization, units, coordinate systems, sampling geometries and topoligies, missing data indicators, and error estimates. When data are combined in computations or visualizations, unit conversion, coordinate transforms and resampling are done implicitly as needed.

Research paper thumbnail of Integration of the ImageJ Ecosystem in KNIME Analytics Platform

Frontiers in Computer Science

Research paper thumbnail of Integration of the ImageJ Ecosystem in KNIME Analytics Platform

Frontiers in Computer Science

Research paper thumbnail of Metadata matters: access to image data in the real world

The Journal of cell biology, Jan 31, 2010

Research paper thumbnail of The ImageJ ecosystem: An open platform for biomedical image analysis

Molecular Reproduction and Development, 2015

Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more pre... more Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is availableÀ Àfrom commercial to academic, special-purpose to Swiss army knife, small to largeÀ Àbut a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the opensoftware platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is selfinfluenced by coevolving projects within the ImageJ ecosystem.

Research paper thumbnail of Fiji: an open-source platform for biological-image analysis

Research paper thumbnail of Metadata matters: access to image data in the real world

The Journal of cell biology, Jan 31, 2010

Data sharing is important in the biological sciences to prevent duplication of effort, to promote... more 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.

Research paper thumbnail of The Open Microscopy Environment: Informatics and Quantitative Analysis for Biological Microscopy

Microscopy and Microanalysis, 2009

Research paper thumbnail of Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software

Research paper thumbnail of Java distributed components for numerical visualization in VisAD

Communications of the ACM, 2005

Research paper thumbnail of VisBio: A Computational Tool for Visualization of Multidimensional Biological Image Data

Research paper thumbnail of Open Source BioImage Informatics: Tools for Interoperability

Research paper thumbnail of Tools for Visualizing Multidimensional Images from Living Specimens

Photochemistry and Photobiology, 2005

Research paper thumbnail of VisBio: a Flexible Open-Source Visualization Package for Multidimensional Image Data

Microscopy Today

Over the past few years there has been a dramatic improvement in microscopy acquisition technique... more Over the past few years there has been a dramatic improvement in microscopy acquisition techniques, in effective imaging modalities as well as raw hardware performance. As the microscopist's available tools become more sophisticated and diverse—e.g., time-lapse, Z sectioning, multispectra, lifetime, nth harmonic, polarization, and many combinations thereof—we face a corresponding increase in complexity in the software for understanding and interpreting the resultant data. With lifetime imaging, for example, it is overwhelming to study the raw numbers; instead, an exponential curve-fitting algorithm must be applied to extract meaningful lifetime values from the mass of photon counts recorded by the instrument.

Research paper thumbnail of Scientific Community Image Forum: A discussion forum for scientific image software

PLOS Biology

Forums and email lists play a major role in assisting scientists in using software. Previously, e... more Forums and email lists play a major role in assisting scientists in using software. Previously, each open-source bioimaging software package had its own distinct forum or email list. Although each provided access to experts from various software teams, this fragmentation resulted in many scientists not knowing where to begin with their projects. Thus, the scientific imaging community lacked a central platform where solutions could be discussed in an open, software-independent manner. In response, we introduce the Scientific Community Image Forum, where users can pose software-related questions about digital image analysis, acquisition, and data management.

Research paper thumbnail of Assessing microscope image focus quality with deep learning

BMC bioinformatics, Jan 15, 2018

Large image datasets acquired on automated microscopes typically have some fraction of low qualit... more Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which we...

Research paper thumbnail of FunImageJ: a Lisp framework for scientific image processing

Bioinformatics (Oxford, England), Jan 2, 2017

FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecos... more FunImageJ is a Lisp framework for scientific image processing built upon the ImageJ software ecosystem. The framework provides a natural functional-style for programming, while accounting for the performance requirements necessary in big data processing commonly encountered in biological image analysis. Freely available plugin to Fiji (http://fiji.sc/#download). Installation and use instructions available at (http://imagej.net/FunImageJ). kharrington@uidaho.edu. Supplementary data are available at Bioinformatics online.

Research paper thumbnail of The ImageJ Ecosystem: An Open and Extensible Platform for Biomedical Image Analysis

Microscopy and Microanalysis

Research paper thumbnail of ImageJ: Image Analysis Interoperability for the Next Generation of Biological Image Data

Microscopy and Microanalysis

Research paper thumbnail of DIMACS Workshop on Visualization and Data Mining

Research paper thumbnail of The VisAD Java Class Library for Scientific Data and Visualization

ABSTRACT VisAD is a Java class library for interactive and collaborative visualization and analys... more ABSTRACT VisAD is a Java class library for interactive and collaborative visualization and analysis of numerical data. It is designed to support distributed computing and data sharing on the Internet through the use of distributed objects and a very general numerical data model. The data model integrates metadata for data organization, units, coordinate systems, sampling geometries and topoligies, missing data indicators, and error estimates. When data are combined in computations or visualizations, unit conversion, coordinate transforms and resampling are done implicitly as needed.

Research paper thumbnail of Integration of the ImageJ Ecosystem in KNIME Analytics Platform

Frontiers in Computer Science

Research paper thumbnail of Integration of the ImageJ Ecosystem in KNIME Analytics Platform

Frontiers in Computer Science

Research paper thumbnail of Metadata matters: access to image data in the real world

The Journal of cell biology, Jan 31, 2010

Research paper thumbnail of The ImageJ ecosystem: An open platform for biomedical image analysis

Molecular Reproduction and Development, 2015

Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more pre... more Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is availableÀ Àfrom commercial to academic, special-purpose to Swiss army knife, small to largeÀ Àbut a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the opensoftware platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is selfinfluenced by coevolving projects within the ImageJ ecosystem.

Research paper thumbnail of Fiji: an open-source platform for biological-image analysis

Research paper thumbnail of Metadata matters: access to image data in the real world

The Journal of cell biology, Jan 31, 2010

Data sharing is important in the biological sciences to prevent duplication of effort, to promote... more 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.

Research paper thumbnail of The Open Microscopy Environment: Informatics and Quantitative Analysis for Biological Microscopy

Microscopy and Microanalysis, 2009

Research paper thumbnail of Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software

Research paper thumbnail of Java distributed components for numerical visualization in VisAD

Communications of the ACM, 2005

Research paper thumbnail of VisBio: A Computational Tool for Visualization of Multidimensional Biological Image Data

Research paper thumbnail of Open Source BioImage Informatics: Tools for Interoperability

Research paper thumbnail of Tools for Visualizing Multidimensional Images from Living Specimens

Photochemistry and Photobiology, 2005