Software has become as crucial to the enterprise of microscopy as confocal and electron microscopes. This is demanded by the advanced quantitation required in modern biology and by the shear quantity of data acquired. Much of the ImageJ software we develop is publicly available at research.stowers.org/imagejplugins. Please follow the instructions there to follow our Stowers Fiji update site. Published source code is available on our individual GitHub sites. Here are the links to those sites:
Jay Unruh: https://github.com/jayunruh
Sean McKinney: https://github.com/jouyun
Chris Wood: https://github.com/cwood1967
Richard Alexander: https://github.com/richard-alexander
Sarah Smith: https://github.com/sarah-ellen-smith
Below I will describe our high level software initiatives.
Deep learning has rapidly taken over the world of automated image processing. Stowers has invested significantly in the hardware and training sets required for such activities. We are pursuing applications in electron microscopy segmentation, brightfield and fluorescence microscopy segmentation, and high content cell classification.
The trend in microscopy is towards automated acquisition and quantitative high resolution analysis. The combination of these factors requires custom automated pipelines for analysis. Upon publication, we make the source code available on our github sites (see above).
Along with automated analysis, microscopes are continually becoming more automated. We are using microscope vendor-specific scripting capabilities to automate acquisition where possible.