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首页> 外文会议>SPIE Medical Imaging Conference >Crowdsourcing Lung Nodules Detection and Annotation
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Crowdsourcing Lung Nodules Detection and Annotation

机译：众包肺结节检测与注释

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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 arc 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.
机译：我们提出众包作为一种额外的方式，以帮助放射线医师从临床胸部计算机断层扫描（CT）扫描诊断肺癌。更具体地说，引入了一个完整的工作流程，可以通过利用人群的集体智慧来帮助最大化肺结节检测的灵敏度。我们结合了重叠的薄板最大强度投影（TS-MIP）和电影查看的概念，以渲染可以作为批注任务外包给人群的短视频。这些视频是通过将CT切片的重叠TS-MIP通过患者肺部每个象限的深度进行线性插值而生成的。生成的视频被外包给非专家用户的在线社区，在简短的教程之后，他们在这些视频片段中注释了可疑的结节。通过我们的众包工作流程，我们对20个患者CT数据集（包含178个大小在1至30mm之间的肺结节）的肺结节检测灵敏度达到了90％以上。在所有大小的结核病中，总共1021个注释中只有47个假阳性（对于结核病，＞ 4mm的结核病敏感性为96％）。这些结果表明，众包可以是一种强大且可扩展的方式，可以直接或结合计算机辅助检测（CAD）算法来帮助放射科医生筛查肺癌。对于CAD算法，提出的工作流程可以提供高度准确的训练数据，以克服高假阳性率（每次扫描）的问题。我们还首次提供了结节大小和位置的分析，可帮助改进CAD算法。

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