Weakly Supervised Deep Nuclei Segmentation using Points Annotation in Histopathology ImagesDownload PDF

Dec 13, 2018 (edited Jun 24, 2019)MIDL 2019 Conference Full SubmissionReaders: Everyone
  • Keywords: Nuclei segmentation, weak supervision, deep learning, Voronoi diagram, conditional random field
  • Abstract: Nuclei segmentation is a fundamental task in histopathological image analysis. Typically, such segmentation tasks require significant effort to manually generate pixel-wise annotations for fully supervised training. To alleviate the manual effort, in this paper we propose a novel approach using points only annotation. Two types of coarse labels with complementary information are derived from the points annotation, and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized to further refine the model without introducing extra computational complexity during inference. Experimental results on two nuclei segmentation datasets reveal that the proposed method is able to achieve competitive performance compared to the fully supervised counterpart and the state-of-the art methods while requiring significantly less annotation effort. Our code is publicly available.
  • Code Of Conduct: I have read and accept the code of conduct.
  • Remove If Rejected: (optional) Remove submission if paper is rejected.
8 Replies