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Early experiences with crowdsourcing airway annotations in chest CT. / Cheplygina, Veronika; Perez-Rovira, Adria; Kuo, Wieying; Tiddens, Harm A. W. M.; de Bruijne, Marleen.
Deep 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. ed. / Gustavo 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. Springer, 2016. p. 209-218 (Lecture notes in computer science, Vol. 10008).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Cheplygina, V, Perez-Rovira, A, Kuo, W, Tiddens, HAWM
& de Bruijne, M 2016,
Early experiences with crowdsourcing airway annotations in chest CT. in G Carneiro, D Mateus, L Peter, A Bradley, JMRS Tavares, V Belagiannis, JP Papa, JC Nascimento, M Loog, Z Lu, JS Cardoso & J Cornebise (eds),
Deep 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. Springer, Lecture notes in computer science, vol. 10008, pp. 209-218, 1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, Athen, Greece,
21/10/2016.
https://doi.org/10.1007/978-3-319-46976-8_22APA
Cheplygina, V., Perez-Rovira, A., Kuo, W., Tiddens, H. A. W. M.
, & de Bruijne, M. (2016).
Early experiences with crowdsourcing airway annotations in chest CT. In G. 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 (Eds.),
Deep 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 (pp. 209-218). Springer. Lecture notes in computer science Vol. 10008
https://doi.org/10.1007/978-3-319-46976-8_22Vancouver
Cheplygina V, Perez-Rovira A, Kuo W, Tiddens HAWM
, de Bruijne M.
Early experiences with crowdsourcing airway annotations in chest CT. In Carneiro G, Mateus D, Peter L, Bradley A, Tavares JMRS, Belagiannis V, Papa JP, Nascimento JC, Loog M, Lu Z, Cardoso JS, Cornebise J, editors, Deep 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. Springer. 2016. p. 209-218. (Lecture notes in computer science, Vol. 10008).
https://doi.org/10.1007/978-3-319-46976-8_22Author
Cheplygina, Veronika ; Perez-Rovira, Adria ; Kuo, Wieying ; Tiddens, Harm A. W. M. ; de Bruijne, Marleen. / Early experiences with crowdsourcing airway annotations in chest CT. Deep 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. editor / Gustavo 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. Springer, 2016. pp. 209-218 (Lecture notes in computer science, Vol. 10008).
Bibtex
@inproceedings{9ae10e45d7bf4196b4fa98e8f1a3062f,
title = "Early experiences with crowdsourcing airway annotations in chest CT",
abstract = "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.",
author = "Veronika Cheplygina and Adria Perez-Rovira and Wieying Kuo and Tiddens, {Harm A. W. M.} and {de Bruijne}, Marleen",
year = "2016",
doi = "10.1007/978-3-319-46976-8_22",
language = "English",
isbn = "978-3-319-46975-1",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "209--218",
editor = "Gustavo Carneiro and Diana Mateus and Lo{\"i}c Peter and Andrew Bradley and Tavares, {Jo{\~a}o Manuel R. S.} and Vasileios Belagiannis and Papa, {Jo{\~a}o Paulo} and Nascimento, {Jacinto C.} and Marco Loog and Zhi Lu and Cardoso, {Jaime S.} and Juilen Cornebise",
booktitle = "Deep Learning and Data Labeling for Medical Applications",
address = "Switzerland",
note = "null ; Conference date: 21-10-2016 Through 21-10-2016",
}
RIS
TY - GEN
T1 - Early experiences with crowdsourcing airway annotations in chest CT
AU - Cheplygina, Veronika
AU - Perez-Rovira, Adria
AU - Kuo, Wieying
AU - Tiddens, Harm A. W. M.
AU - de Bruijne, Marleen
N1 - Conference code: 1
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-46976-8_22
DO - 10.1007/978-3-319-46976-8_22
M3 - Article in proceedings
AN - SCOPUS:84992489314
SN - 978-3-319-46975-1
T3 - Lecture notes in computer science
SP - 209
EP - 218
BT - Deep Learning and Data Labeling for Medical Applications
A2 - Carneiro, Gustavo
A2 - Mateus, Diana
A2 - Peter, Loïc
A2 - Bradley, Andrew
A2 - Tavares, João Manuel R. S.
A2 - Belagiannis, Vasileios
A2 - Papa, João Paulo
A2 - Nascimento, Jacinto C.
A2 - Loog, Marco
A2 - Lu, Zhi
A2 - Cardoso, Jaime S.
A2 - Cornebise, Juilen
PB - Springer
Y2 - 21 October 2016 through 21 October 2016
ER -
