  In the clinical settings, during digital examination of histopathological
slides, the pathologist annotate the slides by marking the rough boundary
around the suspected tumour region. The marking or annotation is generally
represented as a polygonal boundary that covers the extent of the tumour in the
slide. These polygonal markings are difficult to imitate through CAD techniques
since the tumour regions are heterogeneous and hence segmenting them would
require exhaustive pixel wise ground truth annotation. Therefore, for CAD
analysis, the ground truths are generally annotated by pathologist explicitly
for research purposes. However, this kind of annotation which is generally
required for semantic or instance segmentation is time consuming and tedious.
In this proposed work, therefore, we have tried to imitate pathologist like
annotation by segmenting tumour extents by polygonal boundaries. For polygon
like annotation or segmentation, we have used Active Contours whose vertices or
snake points move towards the boundary of the object of interest to find the
region of minimum energy. To penalize the Active Contour we used modified U-Net
architecture for learning penalization values. The proposed hybrid deep
learning model fuses the modern deep learning segmentation algorithm with
traditional Active Contours segmentation technique. The model is tested against
both state-of-the-art semantic segmentation and hybrid models for performance
evaluation against contemporary work. The results obtained show that the
pathologist like annotation could be achieved by developing such hybrid models
that integrate the domain knowledge through classical segmentation methods like
Active Contours and global knowledge through semantic segmentation deep
learning models.
