Speaker
Description
The Fiji platform, a cornerstone in scientific imaging, has undergone several major developments to meet evolving community needs, focusing on cross-language integration and modernization to enhance its utility and accessibility.
Recent key initiatives include: upgrading Fiji to OpenJDK 21 with the new Jaunch launcher, which supports both JVM and Python runtimes; developing SciJava Ops for standardizing scientific algorithms; introducing the Appose library to facilitate deep learning integration via cross-language interprocess communication; and releasing the napari-imagej plugin to expose Fiji functionality within napari.
The OpenJDK 21 upgrade enables many new JVM-based technologies, such as improved 3D visualization through the sciview plugin. The 1.0.0 release of SciJava Ops marks a milestone in fostering FAIR and extensible algorithms. The Appose library has enabled new plugins like SAMJ, leveraging deep learning methods such as Meta's Segment Anything Model. The napari-imagej plugin allows seamless integration between ImgLib2 and NumPy data structures, further extending Fiji's utility within the Python ecosystem.
These advancements significantly enhance Fiji's interoperability, Python integration, and overall modernization, positioning it to better serve the evolving needs of the scientific imaging community.
| Authors | Curtis Rueden*, Mark Hiner, Edward Evans III, Gabriel Selzer, Kevin Eliceiri |
|---|---|
| Keywords | fiji, imagej, java, python, napari, deep learning |
