• Veronika Cheplygina
  • Adria Perez-Rovira
  • Wieying Kuo
  • Harm A.W.M. Tiddens
  • M de Bruijne
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.
Original languageEnglish
Title of host publicationDeep Learning and Data Labeling for Medical Applications
Subtitle of host publicationFirst International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Proceedings
EditorsG. Carneiro, D. Mateus, L. Peter, A. Bradley, J.M.R.S. Tavares, V. Belagiannis, J.P. Papa, J.C. Nascimento, M. Loog, Z. Lu, J.S. Cardoso, J. Cornebise
Place of PublicationCham
PublisherSpringer
Pages209-2018
Number of pages10
ISBN (Electronic) 978-3-319-46976-8
ISBN (Print)978-3-319-46975-1
DOIs
Publication statusPublished - 2016
EventFirst International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016: Held in Conjunction with MICCAI 2016 - Athens, Greece
Duration: 21 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science
Volume10008
ISSN (Print)0302-9743

Workshop

WorkshopFirst International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016
CountryGreece
CityAthens
Period21/10/1621/10/16

ID: 11606755