Histological image processing features induce a quantitative characterization of chronic tumor hypoxia

Andrew Sundstrom, Elda Grabocka, Dafna Bar-Sagi, Bud Mishra

Research output: Contribution to journalArticle

Abstract

Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images.

Original languageEnglish (US)
Article numbere0153623
JournalPloS one
Volume11
Issue number4
DOIs
StatePublished - Apr 2016

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Fingerprint Dive into the research topics of 'Histological image processing features induce a quantitative characterization of chronic tumor hypoxia'. Together they form a unique fingerprint.

  • Cite this