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Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy

This review of our experience in computer-assisted tissue image analysis (CATIA) research shows that significant information can be extracted and used to diagnose and distinguish normal from abnormal endometrium. CATIA enabled the evaluation and differentiation between the benign and malignant endom...

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Autores principales: Tanos, Vasilios, Neofytou, Marios, Tanos, Panayiotis, Pattichis, Constantinos S., Pattichis, Marios S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9656747/
https://www.ncbi.nlm.nih.gov/pubmed/36361573
http://dx.doi.org/10.3390/ijms232112782
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author Tanos, Vasilios
Neofytou, Marios
Tanos, Panayiotis
Pattichis, Constantinos S.
Pattichis, Marios S.
author_facet Tanos, Vasilios
Neofytou, Marios
Tanos, Panayiotis
Pattichis, Constantinos S.
Pattichis, Marios S.
author_sort Tanos, Vasilios
collection PubMed
description This review of our experience in computer-assisted tissue image analysis (CATIA) research shows that significant information can be extracted and used to diagnose and distinguish normal from abnormal endometrium. CATIA enabled the evaluation and differentiation between the benign and malignant endometrium during diagnostic hysteroscopy. The efficacy of texture analysis in the endometrium image during hysteroscopy was examined in 40 women, where 209 normal and 209 abnormal regions of interest (ROIs) were extracted. There was a significant difference between normal and abnormal endometrium for the statistical features (SF) features mean, variance, median, energy and entropy; for the spatial grey-level difference matrix (SGLDM) features contrast, correlation, variance, homogeneity and entropy; and for the gray-level difference statistics (GLDS) features homogeneity, contrast, energy, entropy and mean. We further evaluated 52 hysteroscopic images of 258 normal and 258 abnormal endometrium ROIs, and tissue diagnosis was verified by histopathology after biopsy. The YCrCb color system with SF, SGLDM and GLDS color texture features based on support vector machine (SVM) modeling correctly classified 81% of the cases with a sensitivity and a specificity of 78% and 81%, respectively, for normal and hyperplastic endometrium. New technical and computational advances may improve optical biopsy accuracy and assist in the precision of lesion excision during hysteroscopy. The exchange of knowledge, collaboration, identification of tasks and CATIA method selection strategy will further improve computer-aided diagnosis implementation in the daily practice of hysteroscopy.
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spelling pubmed-96567472022-11-15 Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy Tanos, Vasilios Neofytou, Marios Tanos, Panayiotis Pattichis, Constantinos S. Pattichis, Marios S. Int J Mol Sci Communication This review of our experience in computer-assisted tissue image analysis (CATIA) research shows that significant information can be extracted and used to diagnose and distinguish normal from abnormal endometrium. CATIA enabled the evaluation and differentiation between the benign and malignant endometrium during diagnostic hysteroscopy. The efficacy of texture analysis in the endometrium image during hysteroscopy was examined in 40 women, where 209 normal and 209 abnormal regions of interest (ROIs) were extracted. There was a significant difference between normal and abnormal endometrium for the statistical features (SF) features mean, variance, median, energy and entropy; for the spatial grey-level difference matrix (SGLDM) features contrast, correlation, variance, homogeneity and entropy; and for the gray-level difference statistics (GLDS) features homogeneity, contrast, energy, entropy and mean. We further evaluated 52 hysteroscopic images of 258 normal and 258 abnormal endometrium ROIs, and tissue diagnosis was verified by histopathology after biopsy. The YCrCb color system with SF, SGLDM and GLDS color texture features based on support vector machine (SVM) modeling correctly classified 81% of the cases with a sensitivity and a specificity of 78% and 81%, respectively, for normal and hyperplastic endometrium. New technical and computational advances may improve optical biopsy accuracy and assist in the precision of lesion excision during hysteroscopy. The exchange of knowledge, collaboration, identification of tasks and CATIA method selection strategy will further improve computer-aided diagnosis implementation in the daily practice of hysteroscopy. MDPI 2022-10-24 /pmc/articles/PMC9656747/ /pubmed/36361573 http://dx.doi.org/10.3390/ijms232112782 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Tanos, Vasilios
Neofytou, Marios
Tanos, Panayiotis
Pattichis, Constantinos S.
Pattichis, Marios S.
Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy
title Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy
title_full Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy
title_fullStr Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy
title_full_unstemmed Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy
title_short Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy
title_sort computer-aided diagnosis by tissue image analysis as an optical biopsy in hysteroscopy
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9656747/
https://www.ncbi.nlm.nih.gov/pubmed/36361573
http://dx.doi.org/10.3390/ijms232112782
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