  Gland segmentation is a critical step to quantitatively assess the morphology
of glands in histopathology image analysis. However, it is challenging to
separate densely clustered glands accurately. Existing deep learning-based
approaches attempted to use contour-based techniques to alleviate this issue
but only achieved limited success. To address this challenge, we propose a
novel topology-aware network (TA-Net) to accurately separate densely clustered
and severely deformed glands. The proposed TA-Net has a multitask learning
architecture and enhances the generalization of gland segmentation by learning
shared representation from two tasks: instance segmentation and gland topology
estimation. The proposed topology loss computes gland topology using gland
skeletons and markers. It drives the network to generate segmentation results
that comply with the true gland topology. We validate the proposed approach on
the GlaS and CRAG datasets using three quantitative metrics, F1-score,
object-level Dice coefficient, and object-level Hausdorff distance. Extensive
experiments demonstrate that TA-Net achieves state-of-the-art performance on
the two datasets. TA-Net outperforms other approaches in the presence of
densely clustered glands.
