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Epithelial Cell Segmentation via Shape Ranking

  • Alberto Santamaria-PangEmail author
  • Yuchi Huang
  • Zhengyu Pang
  • Li Qing
  • Jens Rittscher
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 14)

Abstract

We present a robust and high-throughput computational method for cell segmentation using multiplexed immunohistopathology images. The major challenges in obtaining an accurate cell segmentation from tissue samples are due to (i) complex cell and tissue morphology, (ii) different sources of variability including non-homogeneous staining and microscope specific noise, and (iii) tissue quality. Here we present a fast method that uses cell shape and scale information via unsupervised machine learning to enhance and improve general purpose segmentation methods. The proposed method is well suited for tissue cytology because it captures the the morphological and shape heterogeneity in different cell populations. We discuss our segmentation framework for analysing approximately one hundred images of lung and colon cancer and we restrict our analysis to epithelial cells.

Keywords

Markov Random Field Shape Descriptor Nottingham Prognostic Index Cell Segmentation Watershed Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work has been developed as part of a larger interdisciplinary research program led by Fiona Ginty. In particular we would like to thank Michael Gerdes, Anup Sood, Christopher Sevinsky, and Brian Sarachan for valuable feedback. Without their collaboration it would have not been possible to evaluate the cell segmentation framework on such vast array of tissue samples. Throughout these studies they have guided our thinking on how more robust and reliable methods could be developed. This work was performed while Yuchi Huang was in GE Global Research.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alberto Santamaria-Pang
    • 1
    Email author
  • Yuchi Huang
    • 1
  • Zhengyu Pang
    • 1
  • Li Qing
    • 1
  • Jens Rittscher
    • 1
  1. 1.GE Global ResearchNiskayunaUSA

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