Fig. 1: The user interface of the segmentation tool (available via the web).

a The left <Segment WSI > column shows the controls for the segmentation plugin: <IO> is required arguments and <WSI Analysis> contains optional parameters. WSI stands for whole slide image and IO stands for Input/Output. The right column shows the WSI viewer controls and annotations created by the plugin. The green annotations are computationally predicted and are easily editable by the user. Slides are analyzed by clicking the <Submit> button in the top left corner. b The options from the <Train Segmentation Network> plugin. Under the <IO> section, a user can specify a directory full of annotated WSIs to use for network training with the <Training Data Folder> option, and where to save the trained model with the <Output Model Name> option. The <Training layers> option gives users the ability to choose which annotation layers should be used for training and multi-class segmentation models can be trained. To speed up the training process, a previously trained segmentation model can be used for transfer learning by specifying the <Input Model File>. Hyperparameters for training the network is automatically set to defaults that work well but can be modified using the options in the <WSI Training Parameters> section. c shows the <Extract Features> plugin which can be used to extract image and morphology features from annotated objects. These features are written to the slide metadata and can be plotted from within the online interface via the <Metadata Plot> tab (on the right). d shows the welcome screen of the online interface athena.ccr.buffalo.edu.