Müller matrix polarimetry for pancreatic tissue characterization

Polarimetry is an optical characterization technique capable of analyzing the polarization state of light reflected by materials and biological samples. In this study, we investigate the potential of Müller matrix polarimetry (MMP) to analyze fresh pancreatic tissue samples. Due to its highly hetero...

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Bibliographic Details
Published in:Scientific reports Vol. 13; no. 1; pp. 16417 - 9
Main Authors: Sampaio, Paulo, Lopez-Antuña, Maria, Storni, Federico, Wicht, Jonatan, Sökeland, Greta, Wartenberg, Martin, Márquez-Neila, Pablo, Candinas, Daniel, Demory, Brice-Olivier, Perren, Aurel, Sznitman, Raphael
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 29.09.2023
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/1305
631/114/1564
639/624/1107/328/1652
639/624/1107/510
692/308/575
692/699/1503/1712
Biological samples
Clinical outcomes
Histopathology
Humanities and Social Sciences
Light
Machine learning
Medicine
Melanoma
multidisciplinary
Optical properties
Pancreas
Pancreatic diseases
Patients
Science
Science (multidisciplinary)
Surgery
Tissues
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Polarimetry is an optical characterization technique capable of analyzing the polarization state of light reflected by materials and biological samples. In this study, we investigate the potential of Müller matrix polarimetry (MMP) to analyze fresh pancreatic tissue samples. Due to its highly heterogeneous appearance, pancreatic tissue type differentiation is a complex task. Furthermore, its challenging location in the body makes creating direct imaging difficult. However, accurate and reliable methods for diagnosing pancreatic diseases are critical for improving patient outcomes. To this end, we measured the Müller matrices of ex-vivo unfixed human pancreatic tissue and leverage the feature-learning capabilities of a machine-learning model to derive an optimized data representation that minimizes normal-abnormal classification error. We show experimentally that our approach accurately differentiates between normal and abnormal pancreatic tissue. This is, to our knowledge, the first study to use ex-vivo unfixed human pancreatic tissue combined with feature-learning from raw Müller matrix readings for this purpose.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-43195-7