Morphology-inspired Unsupervised Gland Segmentation via Selective
  Semantic Grouping
Qixiang Zhang Yi Li Cheng Xue Xiaomeng Li
Designing deep learning algorithms for gland segmentation is crucial for
automatic cancer diagnosis and prognosis, yet the expensive annotation cost
hinders the development and application of this technology. In this paper, we
make a first attempt to explore a deep learning method for unsupervised gland
segmentation, where no manual annotations are required. Existing unsupervised
semantic segmentation methods encounter a huge challenge on gland images: They
either over-segment a gland into many fractions or under-segment the gland
regions by confusing many of them with the background. To overcome this
challenge, our key insight is to introduce an empirical cue about gland
morphology as extra knowledge to guide the segmentation process. To this end,
we propose a novel Morphology-inspired method via Selective Semantic Grouping.
We first leverage the empirical cue to selectively mine out proposals for gland
sub-regions with variant appearances. Then, a Morphology-aware Semantic
Grouping module is employed to summarize the overall information about the
gland by explicitly grouping the semantics of its sub-region proposals. In this
way, the final segmentation network could learn comprehensive knowledge about
glands and produce well-delineated, complete predictions. We conduct
experiments on GlaS dataset and CRAG dataset. Our method exceeds the
second-best counterpart over 10.56% at mIOU.