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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...

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Autores principales: Liu, Xuan, Ouellette, Samantha, Jamgochian, Marielle, Liu, Yuwei, Rao, Babar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
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.
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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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