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Learning and Incorporating Shape Models for Semantic Segmentation

  • H. Ravishankar
  • R. VenkataramaniEmail author
  • S. Thiruvenkadam
  • P. Sudhakar
  • V. Vaidya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Semantic segmentation has been popularly addressed using Fully convolutional networks (FCN) (e.g. U-Net) with impressive results and has been the forerunner in recent segmentation challenges. However, FCN approaches do not necessarily incorporate local geometry such as smoothness and shape, whereas traditional image analysis techniques have benefitted greatly by them in solving segmentation and tracking problems. In this work, we address the problem of incorporating shape priors within the FCN segmentation framework. We demonstrate the utility of such a shape prior in robust handling of scenarios such as loss of contrast and artifacts. Our experiments show \(\approx 5\%\) improvement over U-Net for the challenging problem of ultrasound kidney segmentation.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • H. Ravishankar
    • 1
  • R. Venkataramani
    • 1
    Email author
  • S. Thiruvenkadam
    • 1
  • P. Sudhakar
    • 1
  • V. Vaidya
    • 1
  1. 1.GE Global ResearchBangaloreIndia

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