# Early Experiences with Crowdsourcing Airway Annotations in Chest CT

## TL;DR

This study explores the feasibility of using crowdsourcing to annotate airway structures in chest CT images, aiming to facilitate disease characterization and improve machine learning training data.

## Contribution

It demonstrates that untrained crowd workers can interpret medical images and produce useful airway annotations, highlighting both potential and challenges of crowdsourcing in medical imaging.

## Key findings

- Crowd workers can interpret chest CT images for airway annotation.
- Usable annotations correlate well with expert measurements.
- Instructions need simplification to reduce unusable data.

## 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.