A two-layer structure prediction framework for microscopy cell detection

Comput Med Imaging Graph. 2015 Apr:41:29-36. doi: 10.1016/j.compmedimag.2014.07.001. Epub 2014 Jul 15.

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.

Keywords: Computer vision; Layered models; Machine learning; Microscopy cell detection; Structural prediction.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Breast Neoplasms / pathology*
  • Cell Tracking / methods*
  • Female
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Lymph Nodes / pathology*
  • Lymphatic Metastasis
  • Machine Learning
  • Microscopy / methods*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity