Fully automatic wound segmentation with deep convolutional neural networks

Abstract Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and trea...

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Autores principales:	Chuanbo Wang, D. M. Anisuzzaman, Victor Williamson, Mrinal Kanti Dhar, Behrouz Rostami, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/56bf9fb3b4b6459a84419317353c6c89
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spelling	oai:doaj.org-article:56bf9fb3b4b6459a84419317353c6c892021-12-02T11:57:57ZFully automatic wound segmentation with deep convolutional neural networks10.1038/s41598-020-78799-w2045-2322https://doaj.org/article/56bf9fb3b4b6459a84419317353c6c892020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78799-whttps://doaj.org/toc/2045-2322Abstract Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. The advantage of this model is its lightweight and less compute-intensive architecture. The performance is not compromised and is comparable to deeper neural networks. We build an annotated wound image dataset consisting of 1109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks. The full implementation is available at https://github.com/uwm-bigdata/wound-segmentation .Chuanbo WangD. M. AnisuzzamanVictor WilliamsonMrinal Kanti DharBehrouz RostamiJeffrey NiezgodaSandeep GopalakrishnanZeyun YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chuanbo Wang D. M. Anisuzzaman Victor Williamson Mrinal Kanti Dhar Behrouz Rostami Jeffrey Niezgoda Sandeep Gopalakrishnan Zeyun Yu Fully automatic wound segmentation with deep convolutional neural networks
description	Abstract Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. The advantage of this model is its lightweight and less compute-intensive architecture. The performance is not compromised and is comparable to deeper neural networks. We build an annotated wound image dataset consisting of 1109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks. The full implementation is available at https://github.com/uwm-bigdata/wound-segmentation .
format	article
author	Chuanbo Wang D. M. Anisuzzaman Victor Williamson Mrinal Kanti Dhar Behrouz Rostami Jeffrey Niezgoda Sandeep Gopalakrishnan Zeyun Yu
author_facet	Chuanbo Wang D. M. Anisuzzaman Victor Williamson Mrinal Kanti Dhar Behrouz Rostami Jeffrey Niezgoda Sandeep Gopalakrishnan Zeyun Yu
author_sort	Chuanbo Wang
title	Fully automatic wound segmentation with deep convolutional neural networks
title_short	Fully automatic wound segmentation with deep convolutional neural networks
title_full	Fully automatic wound segmentation with deep convolutional neural networks
title_fullStr	Fully automatic wound segmentation with deep convolutional neural networks
title_full_unstemmed	Fully automatic wound segmentation with deep convolutional neural networks
title_sort	fully automatic wound segmentation with deep convolutional neural networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/56bf9fb3b4b6459a84419317353c6c89
work_keys_str_mv	AT chuanbowang fullyautomaticwoundsegmentationwithdeepconvolutionalneuralnetworks AT dmanisuzzaman fullyautomaticwoundsegmentationwithdeepconvolutionalneuralnetworks AT victorwilliamson fullyautomaticwoundsegmentationwithdeepconvolutionalneuralnetworks AT mrinalkantidhar fullyautomaticwoundsegmentationwithdeepconvolutionalneuralnetworks AT behrouzrostami fullyautomaticwoundsegmentationwithdeepconvolutionalneuralnetworks AT jeffreyniezgoda fullyautomaticwoundsegmentationwithdeepconvolutionalneuralnetworks AT sandeepgopalakrishnan fullyautomaticwoundsegmentationwithdeepconvolutionalneuralnetworks AT zeyunyu fullyautomaticwoundsegmentationwithdeepconvolutionalneuralnetworks
_version_	1718394748329263104

Fully automatic wound segmentation with deep convolutional neural networks

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