Early experiences with crowdsourcing airway annotations in chest CT

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated data to perform well. We investigate whether crowdsourcing can be used to gather airway annotations which can serve directly for measuring the airways, or as training data for the algorithms. We generate image slices at known locations of airways and request untrained crowd workers to outline the airway lumen and airway wall. Our results show that the workers are able to interpret the images, but that the instructions are too complex, leading to many unusable annotations. After excluding unusable annotations, quantitative results show medium to high correlations with expert measurements of the airways. Based on this positive experience, we describe a number of further research directions and provide insight into the challenges of crowdsourcing in medical images from the perspective of first-time users.

OriginalsprogEngelsk
TitelDeep Learning and Data Labeling for Medical Applications : First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings
RedaktørerGustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Juilen Cornebise
Antal sider10
ForlagSpringer
Publikationsdato2016
Sider209-218
ISBN (Trykt)978-3-319-46975-1
ISBN (Elektronisk)978-3-319-46976-8
DOI
StatusUdgivet - 2016
Begivenhed1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - Athen, Grækenland
Varighed: 21 okt. 201621 okt. 2016
Konferencens nummer: 1

Konference

Konference1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis
Nummer1
LandGrækenland
ByAthen
Periode21/10/201621/10/2016
NavnLecture notes in computer science
Vol/bind10008
ISSN0302-9743

ID: 172022844