Methodology for automatic detection of lung nodules in computerized tomography images
Methodology for automatic detection of lung nodules in computerized tomography images
复制标题
DOI：
10.1016/j.cmpb.2009.07.006
复制
发表时间：
2010-04-01
影响因子：
6.1
通讯作者：
Nunes, Rodolfo Acatauassu
中科院分区：
文献类型：
--
作者：
Ferreira da Silva Sousa, Jaao Rodrigo;Silva, Aristofanes Correa;Nunes, Rodolfo Acatauassu
肺癌是一种在世界上几个国家中具有显著患病率的疾病。肺结节的治疗难度大，病情发展迅速，导致患者死亡率非常高。为了提供一种计算替代方案来帮助检测结节，作为专家的第二意见，这项工作提出了一种基于连续检测细化阶段的全自动方法。自动肺结节检测方案包括六个阶段：胸部提取、肺提取、肺重建、结构提取、管状结构消除和假阳性减少。在胸部提取阶段，丢弃患者身体外部的所有伪影。肺提取阶段负责识别肺实质。肺重建阶段的目的是防止不正确地消除属于实质的部分。结构提取阶段包括从肺实质内部选择致密结构。下一阶段，管状结构的消除消除了大部分的肺树。最后，假阳性阶段仅选择具有大概率为结节的结构。每一个阶段都有非常具体的目标，以检测特定的肺结节病例，即使在困难的检测情况下也能确保良好的匹配率。我们使用了33个具有不同诊断和切片数量的检查来验证该方法。我们得到了每项检查的假阳性率为0.42，假阴性率为0.15。从结节候选者中测得的总分类灵敏度为84.84%。特异性为96.15%，总准确率为95.21%。(C)2009爱思唯尔爱尔兰有限公司保留所有权利。
Lung cancer is a disease with significant prevalence in several countries around the world. Its difficult treatment and rapid progression make the mortality rates among people affected by this illness to be very high.Aiming to offer a computational alternative for helping in detection of nodules, serving as a second opinion to the specialists, this work proposes a totally automatic methodology based on successive detection refining stages.The automated lung nodules detection scheme consists of six stages: thorax extraction, lung extraction, lung reconstruction, structures extraction, tubular structures elimination, and false positive reduction. In the thorax extraction stage all the artifacts external to the patient's body are discarded. Lung extraction stage is responsible for the identification of the lung parenchyma. The objective of the lung reconstruction stage is to prevent incorrect elimination of portions belonging to the parenchyma. Structures extraction stage comprises the selection of dense structures from inside the lung parenchyma. The next stage, tubular structures elimination eliminates a great part of the pulmonary trees. Finally, the false positive stage selects only structures with great probability to be nodule. Each of the several stages has very specific objectives in detection of particular cases of lung nodules, ensuring good matching rates even in difficult detection situations.We use 33 exams with diversified diagnosis and slices numbers for validating the methodology. We obtained a false positive per exam rate of 0.42 and false negative rate of 0.15. The total classification sensitivity obtained, measured out of the nodule candidates, was 84.84%. The specificity achieved was 96.15% and the total accuracy of the method was 95.21%. (C) 2009 Elsevier Ireland Ltd. All rights reserved.