The generation of ground truth maps from digital whole slide image subject matter remains a labour-intensive and technically challenging task, with the net effect being that the curation of large libraries of consistently and accurately tagged features remains a daunting prospect for most histologybased machine vision research teams. To address this need, we have developed separate assisted and autonomous image segmentation computational pipelines, which both exhibit the property of converging on a highly generalizable classifier with a high area under the curve (AUC) solution, utilizing far fewer training image set tiles than typically required for deep learning schema. Initial histology feature segmentation and classification efforts with these tools have demonstrated compelling sensitivity and specificity, when compared against pixel-level ground truth maps, as generated by a panel of subject matter experts. Typical AUC values exceed 0.98 and the use of GPU-based computational clusters allows for segmentation activities to be carried out in real time, facilitating the discovery process. Interactive application demonstrations will be available as a component of the showcasing of this abstract.