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A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11435)

Abstract

Nuclei segmentation is an important but challenging task in the analysis of hematoxylin and eosin (H&E)-stained tissue sections. While various segmentation methods have been proposed, machine learning-based algorithms and in particular deep learning-based models have been shown to deliver better segmentation performance. In this work, we propose a novel approach to segment touching nuclei in H&E-stained microscopic images using U-Net-based models in two sequential stages. In the first stage, we perform semantic segmentation using a classification U-Net that separates nuclei from the background. In the second stage, the distance map of each nucleus is created using a regression U-Net. The final instance segmentation masks are then created using a watershed algorithm based on the distance maps. Evaluated on a publicly available dataset containing images from various human organs, the proposed algorithm achieves an average aggregate Jaccard index of 56.87%, outperforming several state-of-the-art algorithms applied on the same dataset.

Keywords

Digital pathology Tissue analysis Nuclei segmentation Deep learning U-Net 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for Pathophysiology and Allergy ResearchMedical University of ViennaViennaAustria
  2. 2.Department of Research and DevelopmentTissueGnostics GmbHViennaAustria
  3. 3.Department of Computer ScienceLoughborough UniversityLoughboroughUK
  4. 4.Department of Biomedical Engineering and Health SystemsKTH Royal Institute of TechnologyStockholmSweden

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