Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1117/12.2292563 | Proceedings of SPIE |
Field | DocType | Volume |
CAD,Training set,Lung cancer,Annotation,Pattern recognition,Lung,Computer science,Crowdsourcing,Artificial intelligence,Work flow,False positive paradox | Journal | 10579 |
ISSN | Citations | PageRank |
0277-786X | 2 | 0.37 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeed Boorboor | 1 | 2 | 0.37 |
| Saad Nadeem | 2 | 2 | 0.71 |
| Ji hwan Park | 3 | 19 | 7.05 |
| Kevin Baker | 4 | 6 | 1.54 |
| Arie E. Kaufman | 5 | 9 | 1.47 |