CellProfiler: image analysis software for identifying and quantifying cell phenotypes
TL;DR: The first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler is described, which can address a variety of biological questions quantitatively.
Abstract: Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).
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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
TL;DR: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis that facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system.
Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
43,540 citations
TL;DR: Naive mesenchymal stem cells are shown here to specify lineage and commit to phenotypes with extreme sensitivity to tissue-level elasticity, consistent with the elasticity-insensitive commitment of differentiated cell types.
12,204 citations
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
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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 or methods from "CellProfiler: image analysis softwa..."
...These modules typically appear in the menu system of the application’s user interface, but can be exposed via interoperability mechanisms in many other ways, such as nodes in KNIME or modules in CellProfiler [13]....
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...It is our intent that with the developments detailed in this paper, the synergy between these tools, which include ImageJ, KNIME, CellProfiler, OMERO and others, will only increase as each tool continues to evolve along with current avenues of scientific inquiry, benefiting not only existing users, but new users and communities as well....
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...CellProfiler [13] ImageJ Server* [107] -...
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...This need and ability for method sharing has resulted in a plethora of opensource solutions in bioimage informatics, ranging from image acquisition tools such asμManager [7, 8]; databases such as Bio-Image Semantic Query User Environment (BisQue) [9] and OME Remote Objects (OMERO) [10]; image analysis suites such as Icy [11] and BioImageXD [12]; scientific workflow and pipeline tools such as CellProfiler [13, 14], KoNstanz Information MinEr (KNIME) [15, 16] and Pipeline Pilot [17]; and (3D) rendering applications such as FluoRender [18] and Vaa3D [19]....
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...We have already implemented such integration for several other tools in the SciJava ecosystem, including CellProfiler, KNIME [16], and the OMERO image server [10]....
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References
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01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
20,442 citations
"CellProfiler: image analysis softwa..." refers background in this paper
...CellProfiler measures a large number of features for each identified cell or subcellular compartment, including area, shape, intensity, and texture (each feature is described in Additional data file 4). This includes many standard features [39,40], but also complex measurements like Zernike shape features [41], and Haralick and Gabor texture features [ 42- 44 ], which are described in detail in the online help and manual....
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01 Jan 2004
TL;DR: ImageJ is an open source Java-written program that is used for many imaging applications, including those that that span the gamut from skin analysis to neuroscience, and can read most of the widely used and significant formats used in biomedical images.
Abstract: Wayne Rasband of NIH has created ImageJ, an open source Java-written program that is now at version 1.31 and is used for many imaging applications, including those that that span the gamut from skin analysis to neuroscience. ImageJ is in the public domain and runs on any operating system (OS). ImageJ is easy to use and can do many imaging manipulations. A very large and knowledgeable group makes up the user community for ImageJ. Topics covered are imaging abilities; cross platform; image formats support as of June 2004; extensions, including macros and plug-ins; and imaging library. NIH reports tens of thousands of downloads at a rate of about 24,000 per month currently. ImageJ can read most of the widely used and significant formats used in biomedical images. Manipulations supported are read/write of image files and operations on separate pixels, image regions, entire images, and volumes (stacks in ImageJ). Basic operations supported include convolution, edge detection, Fourier transform, histogram and particle analyses, editing and color manipulation, and more advanced operations, as well as visualization. For assistance in using ImageJ, users e-mail each other, and the user base is highly knowledgeable and will answer requests on the mailing list. A thorough manual with many examples and illustrations has been written by Tony Collins of the Wright Cell Imaging Facility at Toronto Western Research Institute and is available, along with other listed resources, via the Web.
12,060 citations
"CellProfiler: image analysis softwa..." refers methods in this paper
...Prior to the work presented here, the only flexible, opensource biological image analysis package was ImageJ/NIH Image [21]....
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TL;DR: A screening window coefficient, called "Z- factor," is defined, which is reflective of both the assay signal dynamic range and the data variation associated with the signal measurements, and therefore is suitable for assay quality assessment.
Abstract: The ability to identify active compounds (³hits²) from large chemical libraries accurately and rapidly has been the ultimate goal in developing high-throughput screening (HTS) assays. The ability to identify hits from a particular HTS assay depends largely on the suitability or quality of the assay used in the screening. The criteria or parameters for evaluating the ³suitability² of an HTS assay for hit identification are not well defined and hence it still remains difficult to compare the quality of assays directly. In this report, a screening window coefficient, called ³Z-factor,² is defined. This coefficient is reflective of both the assay signal dynamic range and the data variation associated with the signal measurements, and therefore is suitable for assay quality assessment. The Z-factor is a dimensionless, simple statistical characteristic for each HTS assay. The Z-factor provides a useful tool for comparison and evaluation of the quality of assays, and can be utilized in assay optimization and validation.
6,474 citations
"CellProfiler: image analysis softwa..." refers background in this paper
...4 is considered screenable and 1 is an ideal assay) [63-65]....
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TL;DR: A screen based on high-content imaging was developed to identify genes required for mitotic progression in human cancer cells and applied to an arrayed set of 5,000 unique shRNA-expressing lentiviruses that target 1,028 human genes, providing a widely applicable resource for loss-of-function screens.
1,760 citations