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Polarization and depolarization metrics as optical markers in support to histopathology of ex vivo colon tissue

  • Deyan Ivanov
  • , Viktor Dremin
  • , Ekaterina Borisova
  • , Alexander Bykov
  • , Tatiana Novikova
  • , Igor Meglinski
  • , Razvigor Ossikovski
  • Institute of Electronics Bulgarian Academy of Sciences
  • Orel State University
  • Aston University
  • University of Oulu
  • Sechenov First Moscow State Medical University
  • Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)

Abstract

Tissue polarimetry holds great promise to improve the effectiveness of conventional cancer diagnostics and staging, being a fast, minimally invasive, and low-cost optical technique. We introduce an enhanced diagnostic method for ex vivo colon specimens assessment by utilizing Stokes and Mueller matrix polarimetry. The proposed method makes use of experimental Mueller matrices, measured from healthy and tumor zones of a colon specimen, as input data for post-processing algorithms that include physical realisability filtering, symmetric decomposition and estimation of various polarization and depolarization metrics for colon specimen diagnostics. We validated our results with the gold standard histological diagnostics provided by pathologists. It was found that the Stokes-Mueller matrix polarimetry, combined with the appropriate filtering, decomposition algorithms and polarization/depolarization metrics calculations provides relevant optical markers of the colon tissue pathological conditions (healthy versus cancer), as confirmed by histopathology analysis. This approach potentially provides physicians with valuable and complementary information that holds promises in helping with the diagnostics of colon tissue specimens.

Original languageEnglish
Pages (from-to)4560-4572
Number of pages13
JournalBiomedical Optics Express
Volume12
Issue number7
DOIs
Publication statusPublished - 1 Jul 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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