Biological imaging software tools (original) (raw)
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Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets. The aim of this review is to provide an overview of available image analysis methods for live cell imaging, in particular required preprocessing image segmentation, cell tracking and data visualisation methods. The potential opportunities recent advances in machine learning, especially deep learning, and computer vision provide are being discussed. This review includes overview of the different available software packages and toolkits.
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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.
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Recent advances in biological imaging have resulted in an explosion in the quality and quantity of images obtained in a digital format. Developmental biologists are increasingly acquiring beautiful and complex images, thus creating vast image datasets. In the past, patterns in image data have been detected by the human eye. Larger datasets, however, necessitate high-throughput objective analysis tools to computationally extract quantitative information from the images. These tools have been developed in collaborations between biologists, computer scientists, mathematicians and physicists. In this Primer we present a glossary of image analysis terms to aid biologists and briefly discuss the importance of robust image analysis in developmental studies.
Journal of Structural Biology, 1996
ware platform designed from the outset to handle all aspects of modern computerized multidimensional microscopy. This platform provides users with an execution environment in which 5D data (XYZ, wavelength, and time) can be easily manipulated for the purpose of data collection, processing, display, and analysis. During the entire process, powerful data display functions are readily available for extracting complicated three-dimensional information through data visualization. By employing both the shared memory and multitasking features of the UNIX operation system, individual functions can be implemented as separate programs, and multiple programs can access the same data pool simultaneously. This enables users to combine the functionalities of different programs to facilitate each unique data analysis task. Furthermore, by defining an appropriate program execution model, commonly shared functional components such as data display, data I/O and user interface, etc. can be implemented using simple IVE library calls. This dramatically reduces the program development time and ensures consistency throughout the entire software system. As a result, users can quickly master the microscopy software system and new functions can be easily integrated, as different functional requirements arise for different research projects.