SciJava Ops: An Improved Algorithms Framework for Fiji and Beyond
Gabriel J. Selzer, Curtis T. Rueden, Mark C. Hiner, Edward L. Evans, III, David Kolb, Marcel Wiedenmann, Christian Birkhold, Tim-Oliver Buchholz,, Stefan Helfrich, Brian Northan, Alison Walter, Johannes Schindelin, Tobias, Pietzsch, Stephan Saalfeld, Michael R. Berthold

TL;DR
SciJava Ops introduces a flexible, extensible framework that unifies algorithm integration across diverse scientific imaging platforms, enabling seamless interoperability and data adaptation.
Contribution
It extends Fiji's plugin architecture to support cross-platform algorithm integration with automatic data adaptation, enhancing interoperability.
Findings
Enables integration of algorithms from multiple platforms.
Automatically adapts data structures for algorithms.
Supports use outside Fiji and ImageJ ecosystems.
Abstract
Many scientific software platforms provide plugin mechanisms that simplify the integration, deployment, and execution of externally developed functionality. One of the most widely used platforms in the imaging space is Fiji, a popular open-source application for scientific image analysis. Fiji incorporates and builds on the ImageJ and ImageJ2 platforms, which provide a powerful plugin architecture used by thousands of plugins to solve a wide variety of problems. This capability is a major part of Fiji's success, and it has become a widely used biological image analysis tool and a target for new functionality. However, a plugin-based software architecture cannot unify disparate platforms operating on incompatible data structures; interoperability necessitates the creation of adaptation or "bridge" layers to translate data and invoke functionality. As a result, while platforms like Fiji…
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Taxonomy
TopicsScientific Computing and Data Management · Distributed and Parallel Computing Systems
