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Measuring airways in chest computed tomography (CT) images is important forcharacterizing diseases such as cystic fibrosis, yet very time-consuming toperform manually. Machine learning algorithms offer an alternative, but needlarge sets of annotated data to perform well. We investigate whethercrowdsourcing can be used to gather airway annotations which can serve directlyfor measuring the airways, or as training data for the algorithms. We generateimage slices at known locations of airways and request untrained crowd workersto outline the airway lumen and airway wall. Our results show that the workersare 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 measurementsof the airways. Based on this positive experience, we describe a number offurther research directions and provide insight into the challenges ofcrowdsourcing in medical images from the perspective of first-time users.

Early Experiences with Crowdsourcing Airway Annotations in Chest CT