SciJava Ops: An Improved Algorithms Framework for Fiji and Beyond
This page serves as a stable location for the SciJava Ops Paper and associated resources. If you use, extend, or contribute to SciJava Ops, please cite our publication:
Selzer GJ, Rueden CT, Hiner MC, Evans EL, Kolb D, Wiedenmann M, Birkhold C, Buchholz T-O, Helfrich S, Northan B, Walter A, Schindelin J, Pietzsch T, Saalfeld S, Berthold MR and Eliceiri KW. (2024). SciJava Ops: an improved algorithms framework for Fiji and beyond. Front. Bioinform. 4:1435733. doi:10.3389/fbinf.2024.1435733
The following sections highlight some of the use cases found within the paper.
Python (scyjava)
This use case illustrates the ease with which SciJava Ops can be accessed in Python, showcasing OpEnvironment setup and simple image processing. The full workflow can be found in the scyjava use case.
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Fluorescence Lifetime Image Analysis
This use case illustrates how SciJava Ops can be freely extended with additional algorithms libraries, making use of the SciJava framework for convenience and performance in FLIM analysis. The full workflow can be found in the FLIM use case.
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Spatially Adapted Colocalization Analysis
This use case illustrates the novel scientific utility of the SciJava Ops Image library using powerful algorithms for pixel colocalization. The full workflow can be found in the SACA use case.
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Deconvolution
This use case illustrates the novel scientific utility of the SciJava Ops Image library using powerful algorithms for image deconvolution. The full workflow can be found in the deconvolution use case.
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