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Epithelial Cell Segmentation via Shape Ranking

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Shape Analysis in Medical Image Analysis

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((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.

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Notes

  1. 1.

    Tissue micro-arrays (TMAs) is a collection of tissue samples (biopsies) organized in a two-dimensional array. Typically, they contain hundred of samples organized in one or multiple two-dimensional arrays, where each sample has a diameter of approximately of 0.6 mm. The samples are collected using standardized tissue fixation protocols and each sample is embedded in paraffin and can be used as biomarker discovery tool [12–14].

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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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Authors and Affiliations

  1. GE Global Research, Niskayuna, NY, USA

    Alberto Santamaria-Pang, Yuchi Huang, Zhengyu Pang, Li Qing & Jens Rittscher

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  1. Alberto Santamaria-Pang
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  2. Yuchi Huang
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  3. Zhengyu Pang
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  4. Li Qing
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Corresponding author

Correspondence to Alberto Santamaria-Pang .

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Editors and Affiliations

  1. GE Healthcare and University of Western Ontario, London, Ontario, Canada

    Shuo Li

  2. Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Santamaria-Pang, A., Huang, Y., Pang, Z., Qing, L., Rittscher, J. (2014). Epithelial Cell Segmentation via Shape Ranking. In: Li, S., Tavares, J. (eds) Shape Analysis in Medical Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-03813-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-03813-1_10

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