Early Experiences with Crowdsourcing Airway Annotations in Chest CT
This addresses the time-consuming task of manual airway annotation for diseases like cystic fibrosis, but it is incremental as it builds on existing crowdsourcing methods applied to medical imaging.
The study explored using crowdsourcing to annotate airways in chest CT images for disease measurement or training machine learning algorithms, finding that while workers could interpret images, complex instructions led to many unusable annotations, but after exclusion, correlations with expert measurements were medium to high.
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.