  Convolutional neural networks, the state of the art for image segmentation,
have been successfully applied to histology images by many computational
researchers. However, the translatability of this technology to clinicians and
biological researchers is limited due to the complex and undeveloped user
interface of the code, as well as the extensive computer setup required. As an
extension of our previous work (arXiv:1812.07509), we have developed a tool for
segmentation of whole slide images (WSIs) with an easy to use graphical user
interface. Our tool runs a state-of-the-art convolutional neural network for
segmentation of WSIs in the cloud. Our plugin is built on the open source tool
HistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data
management and viewing abilities for WSI datasets. The ability to access this
tool over the internet will facilitate widespread use by computational
non-experts. Users can easily upload slides to a server where our plugin is
installed and perform human in the loop segmentation analysis remotely. This
tool is open source, and has the ability to be adapted to segment of any
pathological structure. For a proof of concept, we have trained it to segment
glomeruli from renal tissue images, achieving an F-score > 0.97 on holdout
tissue slides.
