User friendly, cloud based, whole slide image segmentation

Abstract

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. We have developed a plugin for segmentation of whole slide images (WSIs) with an easy to use graphical user interface. This plugin 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 the segmentation analysis remotely. This plugin is open source and once trained, has the ability to be applied to the segmentation of any pathological structure. For a proof of concept, we have trained it to segment glomeruli from renal tissue images, demonstrating it on holdout tissue slides.

Keywords: WSI segmentation; cloud based analysis; glomeruli; plugin.

Fig. 1.. The graphical user interface of our plugin running in HistomicsTK available via the web.

The left Detect Glomeruli column shows the controls for the plugin: IO is required arguments and WSI Analysis contains optional parameters. The right column shows the WSI viewer controls and annotations created by our plugin. The green annotations on the holdout slide are predicted by our plugin and are easily editable by the user. Slides are easily analyzed by clicking the submit button in the top left corner, which submits a segmentation job, running the Deeplab network on the remote server (where HistomicsTK is installed).

Fig. 2.. Examples of the HistomicsTK user interface.

The left panel shows the “Jobs” page where users can check the status of a running segmentation job. This displays the command line output of the running plugin that has been abstracted away from the user. The top right panel shows how the plugin is accessed from the user interface (highlighted in red) as well as another example glomeruli segmentation by the plugin on a holdout WSI. The bottom right panel shows an example of file management using HistomicsTK. Here users can upload and manage slides and annotations on the remote server.