ClickPoints : an expandable toolbox for scientific image annotation and analysis (original) (raw)
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Computers in biology and medicine, 2015
We present pyOsiriX, a plugin built for the already popular dicom viewer OsiriX that provides users the ability to extend the functionality of OsiriX through simple Python scripts. This approach allows users to integrate the many cutting-edge scientific/image-processing libraries created for Python into a powerful DICOM visualisation package that is intuitive to use and already familiar to many clinical researchers. Using pyOsiriX we hope to bridge the apparent gap between basic imaging scientists and clinical practice in a research setting and thus accelerate the development of advanced clinical image processing. We provide arguments for the use of Python as a robust scripting language for incorporation into larger software solutions, outline the structure of pyOsiriX and how it may be used to extend the functionality of OsiriX, and we provide three case studies that exemplify its utility. For our first case study we use pyOsiriX to provide a tool for smooth histogram display of vo...
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, 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.
PhAT: A flexible open-source GUI-driven toolkit for photometry analysis
ABSTRACTPhotometry approaches detect sensor-mediated changes in fluorescence as a proxy for rapid molecular changes within the brain. As a flexible technique with a relatively low cost to implement, photometry is rapidly being incorporated into neuroscience laboratories. While multiple data acquisition systems for photometry now exist, robust analytical pipelines for the resulting data remain limited. Here we present thePhotometryAnalysisToolkit (PhAT) - a free open source analysis pipeline that provides options for signal normalization, incorporation of multiple data streams to align photometry data with behavior and other events, calculation of event-related changes in fluorescence, and comparison of similarity across fluorescent traces. A graphical user interface (GUI) enables use of this software without prior coding knowledge. In addition to providing foundational analytical tools, PhAT is designed to readily incorporate community-driven development of new modules for more besp...
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 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.
ImageJ2: ImageJ for the next generation of scientific image data
BMC bioinformatics, 2017
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-generat...
Journal of Neuroscience Methods, 2019
Background: The development and increasing adoption of advanced microscopy imaging technologies, including high resolution, multi-dimensional digital photography and multiple fluorescence channel acquisition, as well as the availability of inexpensive terabyte-capacity storage, have enabled research laboratories to pursue neurohistological imaging experiments involving multiple neurochemical probes and experimental conditions covering a variety of brain regions. Analyzing and processing the resulting datasets, composed of hundreds of micrographs, presents challenges in ensuring accuracy and reproducibility under demanding time and training constraints. New method: The 'Custom Macros' plugin suite for ImageJ automates and systematizes user interaction in neurohistological image analysis tasks, including region selection and thresholding, point/object counts, area measurement, batch filter processing, and data review. Written in the accessible ImageJ macro language, the plugin implements a user login-based data storage framework and facilitates inter-laboratory collaboration over cloud file server clients. Results: A macro-based interface approach integrates dozens of novel operations, software interactions, algorithm calls, and background tasks into individual shortcut commands. Every completed procedure generates image, region, and calibrated measurement records that are saved in a standardized folder structure. Comparisons with existing methods: Plugin installation adds startup access to a persistent interface layer of extensive and streamlined functionality that is generalizable to a variety of neurohistological contexts, thus providing an efficient and reliable alternative to the use of analysis software in an unstructured, provisional manner that necessitates repeated menu and plugin interaction. Conclusions: Our free/open-source software provides researchers a straightforward solution to addressing daunting usability and data oversight issues, ultimately making efficient, accessible, and reproducible image analysis methodology attainable for many laboratories.
BioImageIT: Open-source framework for integration of image data-management with analysis
2021
Open science and FAIR principles have become major topics in the field of bioimaging. This is due to both new data acquisition technologies that generate large datasets, and new analysis approaches that automate data mining with high accuracy. Nevertheless, data are rarely shared and rigorously annotated because it requires a lot of manual and tedious management tasks and software packaging. We present BioImageIT, an open-source framework for integrating data management according to FAIR principles with data processing.
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 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.