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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9656747 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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