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Segmentation Approaches for Diabetic Foot Disorders
Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this appl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866807/ https://www.ncbi.nlm.nih.gov/pubmed/33573296 http://dx.doi.org/10.3390/s21030934 |
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author | Arteaga-Marrero, Natalia Hernández, Abián Villa, Enrique González-Pérez, Sara Luque, Carlos Ruiz-Alzola, Juan |
author_facet | Arteaga-Marrero, Natalia Hernández, Abián Villa, Enrique González-Pérez, Sara Luque, Carlos Ruiz-Alzola, Juan |
author_sort | Arteaga-Marrero, Natalia |
collection | PubMed |
description | Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred. |
format | Online Article Text |
id | pubmed-7866807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78668072021-02-07 Segmentation Approaches for Diabetic Foot Disorders Arteaga-Marrero, Natalia Hernández, Abián Villa, Enrique González-Pérez, Sara Luque, Carlos Ruiz-Alzola, Juan Sensors (Basel) Article Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred. MDPI 2021-01-30 /pmc/articles/PMC7866807/ /pubmed/33573296 http://dx.doi.org/10.3390/s21030934 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Arteaga-Marrero, Natalia Hernández, Abián Villa, Enrique González-Pérez, Sara Luque, Carlos Ruiz-Alzola, Juan Segmentation Approaches for Diabetic Foot Disorders |
title | Segmentation Approaches for Diabetic Foot Disorders |
title_full | Segmentation Approaches for Diabetic Foot Disorders |
title_fullStr | Segmentation Approaches for Diabetic Foot Disorders |
title_full_unstemmed | Segmentation Approaches for Diabetic Foot Disorders |
title_short | Segmentation Approaches for Diabetic Foot Disorders |
title_sort | segmentation approaches for diabetic foot disorders |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866807/ https://www.ncbi.nlm.nih.gov/pubmed/33573296 http://dx.doi.org/10.3390/s21030934 |
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