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Automatic wound detection and size estimation using deep learning algorithms

Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the w...

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Autores principales: Carrión, Héctor, Jafari, Mohammad, Bagood, Michelle Dawn, Yang, Hsin-ya, Isseroff, Roslyn Rivkah, Gomez, Marcella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8942216/
https://www.ncbi.nlm.nih.gov/pubmed/35275923
http://dx.doi.org/10.1371/journal.pcbi.1009852
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author Carrión, Héctor
Jafari, Mohammad
Bagood, Michelle Dawn
Yang, Hsin-ya
Isseroff, Roslyn Rivkah
Gomez, Marcella
author_facet Carrión, Héctor
Jafari, Mohammad
Bagood, Michelle Dawn
Yang, Hsin-ya
Isseroff, Roslyn Rivkah
Gomez, Marcella
author_sort Carrión, Héctor
collection PubMed
description Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the wounded area and the healing process in the test animal, often a mouse. These images are then manually observed to determine key metrics —such as wound size progress— relevant to the study. However, this task is a time-consuming and laborious process. In addition, defining the wound edge could be subjective and can vary from one individual to another even among experts. Furthermore, as our understanding of the healing process grows, so does our need to efficiently and accurately track these key factors for high throughput (e.g., over large-scale and long-term experiments). Thus, in this study, we develop a deep learning-based image analysis pipeline that aims to intake non-uniform wound images and extract relevant information such as the location of interest, wound only image crops, and wound periphery size over-time metrics. In particular, our work focuses on images of wounded laboratory mice that are used widely for translationally relevant wound studies and leverages a commonly used ring-shaped splint present in most images to predict wound size. We apply the method to a dataset that was never meant to be quantified and, thus, presents many visual challenges. Additionally, the data set was not meant for training deep learning models and so is relatively small in size with only 256 images. We compare results to that of expert measurements and demonstrate preservation of information relevant to predicting wound closure despite variability from machine-to-expert and even expert-to-expert. The proposed system resulted in high fidelity results on unseen data with minimal human intervention. Furthermore, the pipeline estimates acceptable wound sizes when less than 50% of the images are missing reference objects.
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spelling pubmed-89422162022-03-24 Automatic wound detection and size estimation using deep learning algorithms Carrión, Héctor Jafari, Mohammad Bagood, Michelle Dawn Yang, Hsin-ya Isseroff, Roslyn Rivkah Gomez, Marcella PLoS Comput Biol Research Article Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the wounded area and the healing process in the test animal, often a mouse. These images are then manually observed to determine key metrics —such as wound size progress— relevant to the study. However, this task is a time-consuming and laborious process. In addition, defining the wound edge could be subjective and can vary from one individual to another even among experts. Furthermore, as our understanding of the healing process grows, so does our need to efficiently and accurately track these key factors for high throughput (e.g., over large-scale and long-term experiments). Thus, in this study, we develop a deep learning-based image analysis pipeline that aims to intake non-uniform wound images and extract relevant information such as the location of interest, wound only image crops, and wound periphery size over-time metrics. In particular, our work focuses on images of wounded laboratory mice that are used widely for translationally relevant wound studies and leverages a commonly used ring-shaped splint present in most images to predict wound size. We apply the method to a dataset that was never meant to be quantified and, thus, presents many visual challenges. Additionally, the data set was not meant for training deep learning models and so is relatively small in size with only 256 images. We compare results to that of expert measurements and demonstrate preservation of information relevant to predicting wound closure despite variability from machine-to-expert and even expert-to-expert. The proposed system resulted in high fidelity results on unseen data with minimal human intervention. Furthermore, the pipeline estimates acceptable wound sizes when less than 50% of the images are missing reference objects. Public Library of Science 2022-03-11 /pmc/articles/PMC8942216/ /pubmed/35275923 http://dx.doi.org/10.1371/journal.pcbi.1009852 Text en © 2022 Carrión et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Carrión, Héctor
Jafari, Mohammad
Bagood, Michelle Dawn
Yang, Hsin-ya
Isseroff, Roslyn Rivkah
Gomez, Marcella
Automatic wound detection and size estimation using deep learning algorithms
title Automatic wound detection and size estimation using deep learning algorithms
title_full Automatic wound detection and size estimation using deep learning algorithms
title_fullStr Automatic wound detection and size estimation using deep learning algorithms
title_full_unstemmed Automatic wound detection and size estimation using deep learning algorithms
title_short Automatic wound detection and size estimation using deep learning algorithms
title_sort automatic wound detection and size estimation using deep learning algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8942216/
https://www.ncbi.nlm.nih.gov/pubmed/35275923
http://dx.doi.org/10.1371/journal.pcbi.1009852
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