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One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging
We investigated a method for automatic skin tissue characterization based on optical coherence tomography (OCT) imaging. We developed a manually scanned single fiber OCT instrument to perform in vivo skin imaging and tumor boundary assessment. The goal is to achieve more accurate tissue excision in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845382/ https://www.ncbi.nlm.nih.gov/pubmed/36650283 http://dx.doi.org/10.1038/s41598-023-28155-5 |
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author | Liu, Xuan Ouellette, Samantha Jamgochian, Marielle Liu, Yuwei Rao, Babar |
author_facet | Liu, Xuan Ouellette, Samantha Jamgochian, Marielle Liu, Yuwei Rao, Babar |
author_sort | Liu, Xuan |
collection | PubMed |
description | We investigated a method for automatic skin tissue characterization based on optical coherence tomography (OCT) imaging. We developed a manually scanned single fiber OCT instrument to perform in vivo skin imaging and tumor boundary assessment. The goal is to achieve more accurate tissue excision in Mohs micrographic surgery (MMS) and reduce the time required for MMS. The focus of this study was to develop a novel machine learning classification method to automatically identify abnormal skin tissues through one-class classification. We trained a deep convolutional neural network (CNN) with a U-Net architecture for automatic skin segmentation, used the pre-trained U-Net as a feature extractor, and trained one-class support vector machine (SVM) classifiers to detect abnormal tissues. The novelty of this study is the use of a neural network as a feature extractor and the use of a one-class SVM for abnormal tissue detection. Our approach eliminated the need to engineer the features for classification and eliminated the need to train the classifier with data obtained from abnormal tissues. To validate the effectiveness of the one-class classification method, we assessed the performance of our algorithm using computer synthesized data, and experimental data. We also performed a pilot study on a patient with skin cancer. |
format | Online Article Text |
id | pubmed-9845382 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98453822023-01-19 One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging Liu, Xuan Ouellette, Samantha Jamgochian, Marielle Liu, Yuwei Rao, Babar Sci Rep Article We investigated a method for automatic skin tissue characterization based on optical coherence tomography (OCT) imaging. We developed a manually scanned single fiber OCT instrument to perform in vivo skin imaging and tumor boundary assessment. The goal is to achieve more accurate tissue excision in Mohs micrographic surgery (MMS) and reduce the time required for MMS. The focus of this study was to develop a novel machine learning classification method to automatically identify abnormal skin tissues through one-class classification. We trained a deep convolutional neural network (CNN) with a U-Net architecture for automatic skin segmentation, used the pre-trained U-Net as a feature extractor, and trained one-class support vector machine (SVM) classifiers to detect abnormal tissues. The novelty of this study is the use of a neural network as a feature extractor and the use of a one-class SVM for abnormal tissue detection. Our approach eliminated the need to engineer the features for classification and eliminated the need to train the classifier with data obtained from abnormal tissues. To validate the effectiveness of the one-class classification method, we assessed the performance of our algorithm using computer synthesized data, and experimental data. We also performed a pilot study on a patient with skin cancer. Nature Publishing Group UK 2023-01-17 /pmc/articles/PMC9845382/ /pubmed/36650283 http://dx.doi.org/10.1038/s41598-023-28155-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liu, Xuan Ouellette, Samantha Jamgochian, Marielle Liu, Yuwei Rao, Babar One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging |
title | One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging |
title_full | One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging |
title_fullStr | One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging |
title_full_unstemmed | One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging |
title_short | One-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging |
title_sort | one-class machine learning classification of skin tissue based on manually scanned optical coherence tomography imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845382/ https://www.ncbi.nlm.nih.gov/pubmed/36650283 http://dx.doi.org/10.1038/s41598-023-28155-5 |
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