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Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation

The segmentation of capillaries in human skin in full-field optical coherence tomography (FF-OCT) images plays a vital role in clinical applications. Recent advances in deep learning techniques have demonstrated a state-of-the-art level of accuracy for the task of automatic medical image segmentatio...

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Autores principales: Mekonnen, Bitewulign Kassa, Hsieh, Tung-Han, Tsai, Dian-Fu, Liaw, Shien-Kuei, Yang, Fu-Liang, Huang, Sheng-Lung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068996/
https://www.ncbi.nlm.nih.gov/pubmed/33920273
http://dx.doi.org/10.3390/diagnostics11040685
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author Mekonnen, Bitewulign Kassa
Hsieh, Tung-Han
Tsai, Dian-Fu
Liaw, Shien-Kuei
Yang, Fu-Liang
Huang, Sheng-Lung
author_facet Mekonnen, Bitewulign Kassa
Hsieh, Tung-Han
Tsai, Dian-Fu
Liaw, Shien-Kuei
Yang, Fu-Liang
Huang, Sheng-Lung
author_sort Mekonnen, Bitewulign Kassa
collection PubMed
description The segmentation of capillaries in human skin in full-field optical coherence tomography (FF-OCT) images plays a vital role in clinical applications. Recent advances in deep learning techniques have demonstrated a state-of-the-art level of accuracy for the task of automatic medical image segmentation. However, a gigantic amount of annotated data is required for the successful training of deep learning models, which demands a great deal of effort and is costly. To overcome this fundamental problem, an automatic simulation algorithm to generate OCT-like skin image data with augmented capillary networks (ACNs) in a three-dimensional volume (which we called the ACN data) is presented. This algorithm simultaneously acquires augmented FF-OCT and corresponding ground truth images of capillary structures, in which potential functions are introduced to conduct the capillary pathways, and the two-dimensional Gaussian function is utilized to mimic the brightness reflected by capillary blood flow seen in real OCT data. To assess the quality of the ACN data, a U-Net deep learning model was trained by the ACN data and then tested on real in vivo FF-OCT human skin images for capillary segmentation. With properly designed data binarization for predicted image frames, the testing result of real FF-OCT data with respect to the ground truth achieved high scores in performance metrics. This demonstrates that the proposed algorithm is capable of generating ACN data that can imitate real FF-OCT skin images of capillary networks for use in research and deep learning, and that the model for capillary segmentation could be of wide benefit in clinical and biomedical applications.
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spelling pubmed-80689962021-04-26 Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation Mekonnen, Bitewulign Kassa Hsieh, Tung-Han Tsai, Dian-Fu Liaw, Shien-Kuei Yang, Fu-Liang Huang, Sheng-Lung Diagnostics (Basel) Article The segmentation of capillaries in human skin in full-field optical coherence tomography (FF-OCT) images plays a vital role in clinical applications. Recent advances in deep learning techniques have demonstrated a state-of-the-art level of accuracy for the task of automatic medical image segmentation. However, a gigantic amount of annotated data is required for the successful training of deep learning models, which demands a great deal of effort and is costly. To overcome this fundamental problem, an automatic simulation algorithm to generate OCT-like skin image data with augmented capillary networks (ACNs) in a three-dimensional volume (which we called the ACN data) is presented. This algorithm simultaneously acquires augmented FF-OCT and corresponding ground truth images of capillary structures, in which potential functions are introduced to conduct the capillary pathways, and the two-dimensional Gaussian function is utilized to mimic the brightness reflected by capillary blood flow seen in real OCT data. To assess the quality of the ACN data, a U-Net deep learning model was trained by the ACN data and then tested on real in vivo FF-OCT human skin images for capillary segmentation. With properly designed data binarization for predicted image frames, the testing result of real FF-OCT data with respect to the ground truth achieved high scores in performance metrics. This demonstrates that the proposed algorithm is capable of generating ACN data that can imitate real FF-OCT skin images of capillary networks for use in research and deep learning, and that the model for capillary segmentation could be of wide benefit in clinical and biomedical applications. MDPI 2021-04-10 /pmc/articles/PMC8068996/ /pubmed/33920273 http://dx.doi.org/10.3390/diagnostics11040685 Text en © 2021 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 Article
Mekonnen, Bitewulign Kassa
Hsieh, Tung-Han
Tsai, Dian-Fu
Liaw, Shien-Kuei
Yang, Fu-Liang
Huang, Sheng-Lung
Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation
title Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation
title_full Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation
title_fullStr Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation
title_full_unstemmed Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation
title_short Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation
title_sort generation of augmented capillary network optical coherence tomography image data of human skin for deep learning and capillary segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068996/
https://www.ncbi.nlm.nih.gov/pubmed/33920273
http://dx.doi.org/10.3390/diagnostics11040685
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