Neural networks promise to bring robust, quantitative analysis to medical
fields, but adoption is limited by the technicalities of training these
networks. To address this translation gap between medical researchers and
neural networks in the field of pathology, we have created an intuitive
interface which utilizes the commonly used whole slide image (WSI) viewer,
Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and
display of neural network predictions on WSIs. Leveraging this, we propose the
use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We
track network performance improvements as a function of iteration and quantify
the use of this pipeline for the segmentation of renal histologic findings on
WSIs. More specifically, we present network performance when applied to
segmentation of renal micro compartments, and demonstrate multi-class
segmentation in human and mouse renal tissue slides. Finally, to show the
adaptability of this technique to other medical imaging fields, we demonstrate
its ability to iteratively segment human prostate glands from radiology imaging
data.Comment: 15 pages, 7 figures, 2 supplemental figures (on the last page
