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© Springer International Publishing Switzerland 2014. 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.

Original publication

DOI

10.1007/978-3-319-03813-1_10

Type

Journal article

Journal

Lecture Notes in Computational Vision and Biomechanics

Publication Date

01/01/2014

Volume

14

Pages

315 - 338