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A two-layer structure prediction framework for microscopy cell detection

  • Yan Xu
  • , Weiying Wu
  • , Eric I.Chao Chang
  • , Danny Chen
  • , Jian Mu
  • , Peter P. Lee
  • , Kim R.M. Blenman
  • , Zhuowen Tu*
  • *Corresponding author for this work
  • Microsoft USA
  • Beihang University
  • University of Notre Dame
  • Cancer Immunotherapeutics and Tumor Immunology (CITI)
  • University of California at San Diego

Research output: Contribution to journalArticlepeer-review

Abstract

The task of microscopy cell detection is of great biological and clinical importance. However, existing algorithms for microscopy cell detection usually ignore the large variations of cells and only focus on the shape feature/descriptor design. Here we propose a new two-layer model for cell centre detection by a two-layer structure prediction framework, which is respectively built on classification for the cell centres implicitly using rich appearances and contextual information and explicit structural information for the cells. Experimental results demonstrate the efficiency and effectiveness of the proposed method over competing state-of-the-art methods, providing a viable alternative for microscopy cell detection.

Original languageEnglish
Pages (from-to)29-36
Number of pages8
JournalComputerized Medical Imaging and Graphics
Volume41
DOIs
StatePublished - 1 Apr 2015

Keywords

  • Computer vision
  • Layered models
  • Machine learning
  • Microscopy cell detection
  • Structural prediction

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