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MicroManager 2.0: An Open Platform for Microscopy Image Acquisition


Projects MicroManager, ImageJ
Lead

Kevin Eliceiri (University of Wisconsin, Madison)

Funding Cycle 1

Proposal Summary

To support the open source µManager optical microscopy acquisition platform to improve its architecture, infrastructure, and support to ensure many years of growth, both in user base and capabilities.


Project

MicroManager

MicroManager is an open source software platform for optical microscopy image acquisition: it controls motorized microscopes, scientific cameras, and other equipment and acquires digital images using various techniques. Optical microscopes for biological research are complex and varied systems, often purpose-built for specific experiments, techniques, or samples, combining equipment from multiple manufacturers. Software plays a central role in these systems, integrating hardware control, automation, and digital data acquisition and processing. MicroManager provides both an easy-to-use graphical control interface for microscopists and programming interfaces that enable the creation of novel imaging applications that work with an extremely wide range (approximately 200 families) of available microscope equipment. MicroManager development was started in 2005 with the goal of establishing an open platform for the development and dissemination of novel types of microscopy, as an alternative to commercial acquisition software whose functioning cannot easily be inspected or extended by scientists. Since then, MicroManager has continued to evolve, with code contributions form researchers as well as equipment manufacturers. Scientists from around the world, as well as small businesses, have used MicroManager as a software library to develop novel types of microscopy applications.


Key Personnel

Kevin Eliceiri
Mark Tsuchida
Nico Stuurman
Bing Dai

Project

ImageJ

ImageJ is one of the most commonly used open-source bioimage analysis platforms for multidimensional image data with a focus on scientific imaging. It is an exemplary software toolkit that stays true to the reproducibility of the scientific method. ImageJ as an application has a simple and user-friendly interface with functions to load, display, and save images. It includes many techniques for image processing, such as colocalization, deconvolution, registration, segmentation, visualization, and more. ImageJ’s extensibility is the root of its effectiveness: many advanced image-processing methods are not provided by the core application, but rather are plugins written by specialists in specific fields. A fundamental strength of ImageJ is in users customizing automated workflows via scripts and macros, including running headless on a remote server/cluster. Another key strength of ImageJ is the extensive, diverse, and helpful user community that emphasizes independent learning and is freely accessible.


Key Personnel

Kevin Eliceiri
Mark Tsuchida
Bing Dai