  We present crowdsourcing as an additional modality to aid radiologists in the
diagnosis of lung cancer from clinical chest computed tomography (CT) scans.
More specifically, a complete workflow is introduced which can help maximize
the sensitivity of lung nodule detection by utilizing the collective
intelligence of the crowd. We combine the concept of overlapping thin-slab
maximum intensity projections (TS-MIPs) and cine viewing to render short videos
that can be outsourced as an annotation task to the crowd. These videos are
generated by linearly interpolating overlapping TS-MIPs of CT slices through
the depth of each quadrant of a patient's lung. The resultant videos are
outsourced to an online community of non-expert users who, after a brief
tutorial, annotate suspected nodules in these video segments. Using our
crowdsourcing workflow, we achieved a lung nodule detection sensitivity of over
90% for 20 patient CT datasets (containing 178 lung nodules with sizes between
1-30mm), and only 47 false positives from a total of 1021 annotations on
nodules of all sizes (96% sensitivity for nodules$>$4mm). These results show
that crowdsourcing can be a robust and scalable modality to aid radiologists in
screening for lung cancer, directly or in combination with computer-aided
detection (CAD) algorithms. For CAD algorithms, the presented workflow can
provide highly accurate training data to overcome the high false-positive rate
(per scan) problem. We also provide, for the first time, analysis on nodule
size and position which can help improve CAD algorithms.
