Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset
Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset
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DOI：
10.1016/j.media.2015.02.002
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发表时间：
2015-05-01
影响因子：
10.9
通讯作者：
Tuinstra, Timothy R.
中科院分区：
文献类型：
--
作者：
Messay, Temesguen;Hardie, Russell C.;Tuinstra, Timothy R.
提出了一种新的用于CT的肺结节分割算法。这些系统包括全自动(FA)系统、半自动(SA)系统和混合系统。与大多数传统系统一样，新的FA系统只需要一个用户提供的提示点。另一方面，SA系统代表了需要8个用户提供的控制点的新算法类。这确实增加了用户的负担，但我们展示了产生的系统非常健壮，可以处理各种具有挑战性的情况。拟议的混合系统从FA系统开始。如果需要改进分割结果，则部署SA系统。FA分割引擎有2个自由参数，SA系统有3个。这些参数是在回归神经网络(RNN)指导的搜索过程中针对每个结节自适应确定的。RNN使用为每个候选分割计算的多个特征。我们使用新的肺部图像数据库联盟和图像数据库资源倡议(LIDC-IDRI)数据来训练和测试我们的系统。据我们所知，这是使用新的LIDC-IDRI数据集的首批特定于结核的性能基准之一。我们还将提出的方法的性能与之前报道的几种其他方法使用的相同数据的结果进行了比较。我们的结果表明，所提出的FA系统比最先进的FA系统要好，SA系统比FA系统提供了相当大的提升。(C)2015年提交人。爱思唯尔出版公司(Elsevier B.V.)
We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system. If improved segmentation results are needed, the SA system is then deployed. The FA segmentation engine has 2 free parameters, and the SA system has 3. These parameters are adaptively determined for each nodule in a search process guided by a regression neural network (RNN). The RNN uses a number of features computed for each candidate segmentation. We train and test our systems using the new Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) data. To the best of our knowledge, this is one of the first nodule-specific performance benchmarks using the new LIDC-IDRI dataset. We also compare the performance of the proposed methods with several previously reported results on the same data used by those other methods. Our results suggest that the proposed FA system improves upon the state-of-the-art, and the SA system offers a considerable boost over the FA system. (C) 2015 The Authors. Published by Elsevier B.V.