  From the simple measurement of tissue attributes in pathology workflow to
designing an explainable diagnostic/prognostic AI tool, access to accurate
semantic segmentation of tissue regions in histology images is a prerequisite.
However, delineating different tissue regions manually is a laborious,
time-consuming and costly task that requires expert knowledge. On the other
hand, the state-of-the-art automatic deep learning models for semantic
segmentation require lots of annotated training data and there are only a
limited number of tissue region annotated images publicly available. To obviate
this issue in computational pathology projects and collect large-scale region
annotations efficiently, we propose an efficient interactive segmentation
network that requires minimum input from the user to accurately annotate
different tissue types in the histology image. The user is only required to
draw a simple squiggle inside each region of interest so it will be used as the
guiding signal for the model. To deal with the complex appearance and amorph
geometry of different tissue regions we introduce several automatic and
minimalistic guiding signal generation techniques that help the model to become
robust against the variation in the user input. By experimenting on a dataset
of breast cancer images, we show that not only does our proposed method speed
up the interactive annotation process, it can also outperform the existing
automatic and interactive region segmentation models.
