  Imaging assays of cellular function, especially those using fluorescent
stains, are ubiquitous in the biological and medical sciences. Despite advances
in computer vision, such images are often analyzed using only manual or
rudimentary automated processes. Watershed-based segmentation is an effective
technique for identifying objects in images; it outperforms commonly used image
analysis methods, but requires familiarity with computer-vision techniques to
be applied successfully. In this report, we present and implement a
watershed-based image analysis and classification algorithm in a GUI, enabling
a broad set of users to easily understand the algorithm and adjust the
parameters to their specific needs. As an example, we implement this algorithm
to find and classify cells in a complex imaging assay for mitochondrial
function. In a second example, we demonstrate a workflow using manual
comparisons and receiver operator characteristics to optimize the algorithm
parameters for finding live and dead cells in a standard viability assay.
Overall, this watershed-based algorithm is more advanced than traditional
thresholding and can produce optimized, automated results. By incorporating
associated pre-processing steps in the GUI, the algorithm is also easily
adjusted, rendering it user-friendly.
