Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach

Myeongseop Kim; Hee Ryung Lee; Razvigor Ossikovski; Aude Jobart-Malfait; Dominique Lamarque; Tatiana Novikova

doi:10.1117/12.3021846

Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach

Myeongseop Kim
, Hee Ryung Lee
, Razvigor Ossikovski
, Aude Jobart-Malfait
, Dominique Lamarque
, Tatiana Novikova

Institut polytechnique de Paris
Institute of Biological and Medical Imaging
Technical University of Munich
Université Versailles-Saint Quentin
Florida International University

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

We investigated gastric tissue biopsies using a liquid crystal-based Mueller microscope and a machine-learning approach to examine the degree of inflammation. Machine learning and statistical analysis were performed with the multidimensional dataset including the polarimetric properties (linear retardance and dichroism, and circular depolarization) and total transmitted intensity images of the unstained thin sections of gastric tissue to identify and quantify the microstructural differences between healthy control, chronic gastritis, and gastric cancer.

langue originale	Anglais
titre	Liquid Crystals Optics and Photonic Devices
rédacteurs en chef	Ibrahim Abdulhalim, Camilla Parmeggiani
Editeur	SPIE
ISBN (Electronique)	9781510673502
Les DOIs	https://doi.org/10.1117/12.3021846
état	Publié - 1 janv. 2024
Evénement	Liquid Crystals Optics and Photonic Devices 2024 - Strasbourg, France Durée: 8 avr. 2024 → 11 avr. 2024

Série de publications

Nom	Proceedings of SPIE - The International Society for Optical Engineering
Volume	13016
ISSN (imprimé)	0277-786X
ISSN (Electronique)	1996-756X

Une conférence

Une conférence	Liquid Crystals Optics and Photonic Devices 2024
Pays/Territoire	France
La ville	Strasbourg
période	8/04/24 → 11/04/24

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 3 Bonne santé et bien-être

Accès au document

10.1117/12.3021846

Contient cette citation

Kim, M., Lee, H. R., Ossikovski, R., Jobart-Malfait, A., Lamarque, D., & Novikova, T. (2024). Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach. Dans I. Abdulhalim, & C. Parmeggiani (eds.), Liquid Crystals Optics and Photonic Devices Article 1301607 (Proceedings of SPIE - The International Society for Optical Engineering; Vol 13016). SPIE. https://doi.org/10.1117/12.3021846

Kim, Myeongseop ; Lee, Hee Ryung ; Ossikovski, Razvigor et al. / Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach. Liquid Crystals Optics and Photonic Devices. Editeur / Ibrahim Abdulhalim ; Camilla Parmeggiani. SPIE, 2024. (Proceedings of SPIE - The International Society for Optical Engineering).

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title = "Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach",

abstract = "We investigated gastric tissue biopsies using a liquid crystal-based Mueller microscope and a machine-learning approach to examine the degree of inflammation. Machine learning and statistical analysis were performed with the multidimensional dataset including the polarimetric properties (linear retardance and dichroism, and circular depolarization) and total transmitted intensity images of the unstained thin sections of gastric tissue to identify and quantify the microstructural differences between healthy control, chronic gastritis, and gastric cancer.",

keywords = "Gastric cancer, Mueller microscopy, Optical anisotropy, Statistical image analysis",

author = "Myeongseop Kim and Lee, \{Hee Ryung\} and Razvigor Ossikovski and Aude Jobart-Malfait and Dominique Lamarque and Tatiana Novikova",

note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Liquid Crystals Optics and Photonic Devices 2024 ; Conference date: 08-04-2024 Through 11-04-2024",

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doi = "10.1117/12.3021846",

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Kim, M, Lee, HR, Ossikovski, R, Jobart-Malfait, A, Lamarque, D & Novikova, T 2024, Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach. Dans I Abdulhalim & C Parmeggiani (eds), Liquid Crystals Optics and Photonic Devices., 1301607, Proceedings of SPIE - The International Society for Optical Engineering, VOL. 13016, SPIE, Liquid Crystals Optics and Photonic Devices 2024, Strasbourg, France, 8/04/24. https://doi.org/10.1117/12.3021846

Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach. / Kim, Myeongseop; Lee, Hee Ryung; Ossikovski, Razvigor et al.
Liquid Crystals Optics and Photonic Devices. Ed. / Ibrahim Abdulhalim; Camilla Parmeggiani. SPIE, 2024. 1301607 (Proceedings of SPIE - The International Society for Optical Engineering; Vol 13016).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach

AU - Kim, Myeongseop

AU - Lee, Hee Ryung

AU - Ossikovski, Razvigor

AU - Jobart-Malfait, Aude

AU - Lamarque, Dominique

AU - Novikova, Tatiana

PY - 2024/1/1

Y1 - 2024/1/1

N2 - We investigated gastric tissue biopsies using a liquid crystal-based Mueller microscope and a machine-learning approach to examine the degree of inflammation. Machine learning and statistical analysis were performed with the multidimensional dataset including the polarimetric properties (linear retardance and dichroism, and circular depolarization) and total transmitted intensity images of the unstained thin sections of gastric tissue to identify and quantify the microstructural differences between healthy control, chronic gastritis, and gastric cancer.

AB - We investigated gastric tissue biopsies using a liquid crystal-based Mueller microscope and a machine-learning approach to examine the degree of inflammation. Machine learning and statistical analysis were performed with the multidimensional dataset including the polarimetric properties (linear retardance and dichroism, and circular depolarization) and total transmitted intensity images of the unstained thin sections of gastric tissue to identify and quantify the microstructural differences between healthy control, chronic gastritis, and gastric cancer.

KW - Gastric cancer

KW - Mueller microscopy

KW - Optical anisotropy

KW - Statistical image analysis

U2 - 10.1117/12.3021846

DO - 10.1117/12.3021846

M3 - Conference contribution

AN - SCOPUS:85200116483

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Liquid Crystals Optics and Photonic Devices

A2 - Abdulhalim, Ibrahim

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T2 - Liquid Crystals Optics and Photonic Devices 2024

Y2 - 8 April 2024 through 11 April 2024

ER -

Kim M, Lee HR, Ossikovski R, Jobart-Malfait A, Lamarque D, Novikova T. Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach. Dans Abdulhalim I, Parmeggiani C, rédacteurs en chef, Liquid Crystals Optics and Photonic Devices. SPIE. 2024. 1301607. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.3021846

Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Empreinte digitale

Contient cette citation