Speaker
Description
This project introduces a novel microscope image acquisition plugin for QuPath designed to enhance data-driven image collections by connecting a user-friendly image analysis tool with PycroManager and MicroManager for microscope control. QuPath provides quick and easy tools to generate user-defined annotations within a whole slide image to guide image collections at higher resolutions or with different modalities. Alternatively, acquisition of regions of interest can be automated through scripts. By leveraging the capabilities of QuPath in conjunction with our plugin, researchers can efficiently target and analyze specific tissue areas within large datasets.
The workflow begins with either a bounding box of stage coordinates, or an overview image collected using a slide scanner. After identifying either the location of the tissue on the slide or subregions of tissue, these areas of interest can be automatically imaged at various resolutions or with different modalities. Targeted acquisitions benefit researchers by reducing total data storage and acquisition time. Utilizing physical space coordinates for the image positions also enables multiple microscopes to be used sequentially, and the images correlated at the end for analysis.
| Authors | Michael S. Nelson*, Jenu V. Chacko, Kevin Eliceiri |
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| Keywords | Smart microscopy, data-driven microscopy, run-time analysis, collagen, |
