The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation
dc.contributor.author | Meakin, JR | |
dc.contributor.author | Ames, RM | |
dc.contributor.author | Jeynes, JCG | |
dc.contributor.author | Welsman, J | |
dc.contributor.author | Gundry, M | |
dc.contributor.author | Knapp, K | |
dc.contributor.author | Everson, R | |
dc.date.accessioned | 2019-10-15T09:29:55Z | |
dc.date.issued | 2019-10-10 | |
dc.description.abstract | 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. | en_GB |
dc.description.sponsorship | Wellcome Trust | en_GB |
dc.identifier.citation | Vol. 14 (10), article e0222523 | en_GB |
dc.identifier.doi | 10.1371/journal.pone.0222523 | |
dc.identifier.grantnumber | WT105618MA | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/39200 | |
dc.language.iso | en | en_GB |
dc.publisher | Public Library of Science | en_GB |
dc.relation.url | https://doi.org/10.24378/exe.1703 | en_GB |
dc.rights | © 2019 Meakin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_GB |
dc.subject | Citizen science | en_GB |
dc.subject | Vertebrae | en_GB |
dc.subject | Image analysis | en_GB |
dc.subject | Magnetic resonance imaging | en_GB |
dc.subject | Algorithms | en_GB |
dc.subject | Imaging techniques | en_GB |
dc.subject | Medicine and health sciences | en_GB |
dc.subject | Software tools | en_GB |
dc.title | The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-10-15T09:29:55Z | |
dc.description | This is the final version. Available from Public Library of Science via the DOI in this record. | en_GB |
dc.description | Data cannot be shared publicly because participants did not consent for their data to be made publicly available, however, consent was granted to make the data available to researchers for use in related studies. Further information about the data and details of how to request access are available from the University of Exeter's institutional repository at: https://doi.org/10.24378/exe.1703. | en_GB |
dc.identifier.journal | PLOS ONE | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019-09-02 | |
exeter.funder | ::Wellcome Trust | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2019-09-02 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-10-15T09:22:27Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2019-10-15T09:29:58Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © 2019 Meakin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.