Crowdsourcing Airway Annotations in Chest Computed Tomography Images
This study investigates a potential method for accelerating the creation of large annotated datasets for machine learning algorithms in medical imaging, specifically for airway measurements in chest CT scans, which is an incremental step towards practical application.
This paper explores using crowdsourcing for annotating airways in chest CT scans to address the time-consuming nature of manual annotation for disease characterization. While a significant portion of annotations were excluded due to potential misunderstanding, the remaining crowd-sourced annotations showed moderate to strong correlations with expert measurements, though slightly lower than inter-expert correlations.
Measuring airways in chest computed tomography (CT) scans 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 scans for good performance. We investigate whether crowdsourcing can be used to gather airway annotations. We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall. After combining multiple crowd workers, we compare the measurements to those made by the experts in the original scans. Similar to our preliminary study, a large portion of the annotations were excluded, possibly due to workers misunderstanding the instructions. After excluding such annotations, moderate to strong correlations with the expert can be observed, although these correlations are slightly lower than inter-expert correlations. Furthermore, the results across subjects in this study are quite variable. Although the crowd has potential in annotating airways, further development is needed for it to be robust enough for gathering annotations in practice. For reproducibility, data and code are available online: \url{http://github.com/adriapr/crowdairway.git}.