  Accurate and automated gland segmentation on histology tissue images is an
essential but challenging task in the computer-aided diagnosis of
adenocarcinoma. Despite their prevalence, deep learning models always require a
myriad number of densely annotated training images, which are difficult to
obtain due to extensive labor and associated expert costs related to histology
image annotations. In this paper, we propose the pairwise relation-based
semi-supervised (PRS^2) model for gland segmentation on histology images. This
model consists of a segmentation network (S-Net) and a pairwise relation
network (PR-Net). The S-Net is trained on labeled data for segmentation, and
PR-Net is trained on both labeled and unlabeled data in an unsupervised way to
enhance its image representation ability via exploiting the semantic
consistency between each pair of images in the feature space. Since both
networks share their encoders, the image representation ability learned by
PR-Net can be transferred to S-Net to improve its segmentation performance. We
also design the object-level Dice loss to address the issues caused by touching
glands and combine it with other two loss functions for S-Net. We evaluated our
model against five recent methods on the GlaS dataset and three recent methods
on the CRAG dataset. Our results not only demonstrate the effectiveness of the
proposed PR-Net and object-level Dice loss, but also indicate that our PRS^2
model achieves the state-of-the-art gland segmentation performance on both
benchmarks.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing