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首页> 外文期刊>Medical image analysis >Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge
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Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

机译：验证，比较和算法的自动检测肺结核术中肺结核图像的算法：LUNA16挑战

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Highlights ? A novel objective evaluation framework for nodule detection algorithms using the largest publicly available LIDC-IDRI data set. ? The impact of combining individual systems on the detection performance was investigated. ? The combination of classical candidate detectors and a combination of deep learning architectures generates excellent results, better than any individual system. ? Our observer study has shown that CAD detects nodules that were missed by expert readers. ? We released this set of additional nodules for further development of CAD systems. Graphical abstract Display Omitted Abstract Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.
机译：强调 ？使用最大可公共LIDC-IDRI数据集的结节检测算法的新颖的客观评估框架。还调查了各个系统对检测性能的影响。还古典候选探测器的组合和深度学习架构的组合产生了优异的结果，比任何单独的系统更好。还我们的观察者研究表明，CAD检测专家读者错过的结节。还我们发布了这套额外的结节，以进一步开发CAD系统。图形摘要显示省略摘要胸椎计算断层扫描（CT）肺结核的自动检测扫描是过去二十年的一个活跃的研究领域。但是，只有很少的研究，在公共数据库中提供对不同系统的比较绩效评估。因此，我们已经建立了Luna16挑战，使用胸部CT扫描的最大可公开可用的参考数据库，LIDC-IDRI数据集，这是一个客观评估框架。在Luna16中，参与者在两个轨道之一中开发他们的算法并在888 CT扫描上上传他们的预测：1）应开发完整的CAD系统的完整结节检测轨迹，或者2）提供了一组提供的假阳性减速轨迹结核候选人应分类。本文介绍了Luna16的设置，到目前为止呈现出挑战的结果。此外，还研究了各个系统对检测性能的影响。观察到领先的解决方案采用卷积网络并使用所提供的一组结节候选。这些溶液的组合在每次扫描的较小的1.0误阳性下达到95％的优异敏感性。这突出了组合算法来提高检测性能的可能性。我们的观察员与四名专家读者研究表明，最好的系统检测到最初注释LIDC-IDRI数据的专家读者错过的结节。我们发布了这套额外的结节，以进一步开发CAD系统。

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