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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...

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Autores principales: Arteaga-Marrero, Natalia, Hernández, Abián, Villa, Enrique, González-Pérez, Sara, Luque, Carlos, Ruiz-Alzola, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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