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The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation

The development of automatic methods for segmenting anatomy from medical images is an important goal for many medical and healthcare research areas. Datasets that can be used to train and test computer algorithms, however, are often small due to the difficulties in obtaining experts to segment enoug...

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Autores principales: Meakin, Judith R., Ames, Ryan M., Jeynes, J. Charles G., Welsman, Jo, Gundry, Michael, Knapp, Karen, Everson, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786545/
https://www.ncbi.nlm.nih.gov/pubmed/31600225
http://dx.doi.org/10.1371/journal.pone.0222523
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author Meakin, Judith R.
Ames, Ryan M.
Jeynes, J. Charles G.
Welsman, Jo
Gundry, Michael
Knapp, Karen
Everson, Richard
author_facet Meakin, Judith R.
Ames, Ryan M.
Jeynes, J. Charles G.
Welsman, Jo
Gundry, Michael
Knapp, Karen
Everson, Richard
author_sort Meakin, Judith R.
collection PubMed
description The development of automatic methods for segmenting anatomy from medical images is an important goal for many medical and healthcare research areas. Datasets that can be used to train and test computer algorithms, however, are often small due to the difficulties in obtaining experts to segment enough examples. Citizen science provides a potential solution to this problem but the feasibility of using the public to identify and segment anatomy in a medical image has not been investigated. Our study therefore aimed to explore the feasibility, in terms of performance and motivation, of using citizens for such purposes. Public involvement was woven into the study design and evaluation. Twenty-nine citizens were recruited and, after brief training, asked to segment the spine from a dataset of 150 magnetic resonance images. Participants segmented as many images as they could within three one-hour sessions. Their accuracy was evaluated by comparing them, as individuals and as a combined consensus, to the segmentations of three experts. Questionnaires and a focus group were used to determine the citizens’ motivation for taking part and their experience of the study. Citizen segmentation accuracy, in terms of agreement with the expert consensus segmentation, varied considerably between individual citizens. The citizen consensus, however, was close to the expert consensus, indicating that when pooled, citizens may be able to replace or supplement experts for generating large image datasets. Personal interest and a desire to help were the two most common reasons for taking part in the study.
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spelling pubmed-67865452019-10-19 The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation Meakin, Judith R. Ames, Ryan M. Jeynes, J. Charles G. Welsman, Jo Gundry, Michael Knapp, Karen Everson, Richard PLoS One Research Article The development of automatic methods for segmenting anatomy from medical images is an important goal for many medical and healthcare research areas. Datasets that can be used to train and test computer algorithms, however, are often small due to the difficulties in obtaining experts to segment enough examples. Citizen science provides a potential solution to this problem but the feasibility of using the public to identify and segment anatomy in a medical image has not been investigated. Our study therefore aimed to explore the feasibility, in terms of performance and motivation, of using citizens for such purposes. Public involvement was woven into the study design and evaluation. Twenty-nine citizens were recruited and, after brief training, asked to segment the spine from a dataset of 150 magnetic resonance images. Participants segmented as many images as they could within three one-hour sessions. Their accuracy was evaluated by comparing them, as individuals and as a combined consensus, to the segmentations of three experts. Questionnaires and a focus group were used to determine the citizens’ motivation for taking part and their experience of the study. Citizen segmentation accuracy, in terms of agreement with the expert consensus segmentation, varied considerably between individual citizens. The citizen consensus, however, was close to the expert consensus, indicating that when pooled, citizens may be able to replace or supplement experts for generating large image datasets. Personal interest and a desire to help were the two most common reasons for taking part in the study. Public Library of Science 2019-10-10 /pmc/articles/PMC6786545/ /pubmed/31600225 http://dx.doi.org/10.1371/journal.pone.0222523 Text en © 2019 Meakin et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Meakin, Judith R.
Ames, Ryan M.
Jeynes, J. Charles G.
Welsman, Jo
Gundry, Michael
Knapp, Karen
Everson, Richard
The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation
title The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation
title_full The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation
title_fullStr The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation
title_full_unstemmed The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation
title_short The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation
title_sort feasibility of using citizens to segment anatomy from medical images: accuracy and motivation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786545/
https://www.ncbi.nlm.nih.gov/pubmed/31600225
http://dx.doi.org/10.1371/journal.pone.0222523
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