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.

设计深度学习算法用于腺体分割对于自动癌症诊断和预后至关重要，然而昂贵的注释成本阻碍了该技术的发展和应用。本文首次尝试探索用于无监督腺体分割的深度学习方法，无需手动注释。为了克服语义分割方法在腺体图像上遇到的挑战，我们引入了关于腺体形态的经验性线索作为额外知识来指导分割过程。我们提出了一种新颖的基于形态学启发的选择性语义分组方法，通过该方法，最终分割网络能够学习关于腺体的综合知识，并产生清晰完整的预测。在GlaS和CRAG数据集上进行实验，我们的方法在mIOU上超过次优方法10.56%。

基于形态学启发的无监督腺体分割：选择性语义分组