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A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation

  • Zengqiang Yan
  • Xin Yang
  • Kwang-Ting Tim Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Segmenting gland instance in histology images requires not only separating glands from a complex background but also identifying each gland individually via accurate boundary detection. This is a very challenging task due to lots of noises from the background, tiny gaps between adjacent glands, and the “coalescence” problem arising from adhesive gland instances. State-of-the-art methods adopted multi-channel/multi-task deep models to separately accomplish pixel-wise gland segmentation and boundary detection, yielding a high model complexity and difficulties in training. In this paper, we present a unified deep model with a new shape-preserving loss which facilities the training for both pixel-wise gland segmentation and boundary detection simultaneously. The proposed shape-preserving loss helps significantly reduce the model complexity and make the training process more controllable. Compared with the current state-of-the-art methods, the proposed deep model with the shape-preserving loss achieves the best overall performance on the 2015 MICCAI Gland Challenge dataset. In addition, the flexibility of integrating the proposed shape-preserving loss into any learning based medical image segmentation networks offers great potential for further performance improvement of other applications.

Keywords

Deep convolutional neural network Gland instance segmentation Shape-preserving loss Histology image analysis 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zengqiang Yan
    • 1
  • Xin Yang
    • 2
  • Kwang-Ting Tim Cheng
    • 1
  1. 1.Department of Computer Science and EngineeringThe Hong Kong University of Science and TechnologyKowloonHong Kong
  2. 2.School of Electronic Information and CommunicationsHuazhong University of Science and TechnologyWuhanChina

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