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


Autoři: Judith R. Meakin aff001;  Ryan M. Ames aff002;  J. Charles G. Jeynes aff003;  Jo Welsman aff003;  Michael Gundry aff004;  Karen Knapp aff004;  Richard Everson aff005
Působiště autorů: Biomedical Physics Group, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom aff001;  Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom aff002;  Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom aff003;  Medical Imaging, University of Exeter Medical School, University of Exeter, Exeter, United Kingdom aff004;  Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222523

Souhrn

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.

Klíčová slova:

Algorithms – Citizen science – Image analysis – Imaging techniques – Magnetic resonance imaging – Medicine and health sciences – Software tools – Vertebrae


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PLOS One


2019 Číslo 10

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