An approach for reducing the error rate in automated lung segmentation.
An approach for reducing the error rate in automated lung segmentation.
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DOI：
10.1016/j.compbiomed.2016.06.022
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发表时间：
2016-09-01
影响因子：
7.7
通讯作者：
Beichel, Reinhard R.
中科院分区：
文献类型：
--
作者：
Gill, Gurman;Beichel, Reinhard R.
稳健的肺部分割具有挑战性，特别是当像大型多中心研究所需的那样，需要处理数以万计的肺部CT扫描时。这项工作的目标是开发和评估一种融合两种不同方法的分割结果的方法，以生成比单个输入分割失败率更低的肺部分割结果。作为融合方法的基础，使用了基于区域生长和基于模型的方法生成的肺部分割结果。融合结果是通过比较输入分割结果，并使用经过训练的分类系统有选择地将它们组合而生成的。该方法在一组包含204例正常和患病肺部的不同CT扫描上进行了评估。融合方法得到的骰子系数为0.9855 ± 0.0106，与两种输入分割方法相比，有统计学上的显著改善。此外，还评估了不同分割精度水平下的失败率。例如，当要求肺部分割的骰子系数必须优于0.97时，融合方法的失败率为6.13%。相比之下，区域生长法和基于模型的方法的失败率分别为18.14%和15.69%。因此，所提出的方法提高了肺部分割的质量，这对后续的肺部定量分析很重要。此外，为了能够与其他方法进行比较，还报告了在LOLA11挑战测试集上的结果。
Robust lung segmentation is challenging, especially when tens of thousands of lung CT scans need to be processed, as required by large multi-center studies. The goal of this work was to develop and assess a method for the fusion of segmentation results from two different methods to generate lung segmentations that have a lower failure rate than individual input segmentations. As basis for the fusion approach, lung segmentations generated with a region growing and model-based approach were utilized. The fusion result was generated by comparing input segmentations and selectively combining them using a trained classification system. The method was evaluated on a diverse set of 204 CT scans of normal and diseased lungs. The fusion approach resulted in a Dice coefficient of 0.9855 ± 0.0106 and showed a statistically significant improvement compared to both input segmentation methods. In addition, the failure rate at different segmentation accuracy levels was assessed. For example, when requiring that lung segmentations must have a Dice coefficient of better than 0.97, the fusion approach had a failure rate of 6.13%. In contrast, the failure rate for region growing and model-based methods was 18.14% and 15.69%, respectively. Therefore, the proposed method improves the quality of the lung segmentations, which is important for subsequent quantitative analysis of lungs. Also, to enable a comparison with other methods, results on the LOLA11 challenge test set are reported.
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DOI：
10.1109/tvcg.2010.56
发表时间：
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影响因子：
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