Early experiences with crowdsourcing airway annotations in chest CT

Veronika Cheplygina; Adria Perez-Rovira; Wieying Kuo; Harm A. W. M. Tiddens; Marleen de Bruijne

doi:10.1007/978-3-319-46976-8_22

Early experiences with crowdsourcing airway annotations in chest CT

Veronika Cheplygina^*, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne

^*Corresponding author for this work

Department of Computer Science

12 Citations (Scopus)

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.

Original language	English
Title of host publication	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
Editors	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
Number of pages	10
Publisher	Springer
Publication date	2016
Pages	209-218
ISBN (Print)	978-3-319-46975-1
ISBN (Electronic)	978-3-319-46976-8
DOIs	https://doi.org/10.1007/978-3-319-46976-8_22
Publication status	Published - 2016
Event	1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - Athen, Greece Duration: 21 Oct 2016 → 21 Oct 2016 Conference number: 1

Conference

Conference	1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis
Number	1
Country/Territory	Greece
City	Athen
Period	21/10/2016 → 21/10/2016

Series	Lecture notes in computer science
Volume	10008
ISSN	0302-9743

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10.1007/978-3-319-46976-8_22

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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_22

Early experiences with crowdsourcing airway annotations in chest CT. / Cheplygina, Veronika; Perez-Rovira, Adria; Kuo, Wieying et al.

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

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_22

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). doi: 10.1007/978-3-319-46976-8_22

Cheplygina, Veronika ; Perez-Rovira, Adria ; Kuo, Wieying et al. / 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).

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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.",

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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.

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Early experiences with crowdsourcing airway annotations in chest CT

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