Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique
Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique
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DOI：
10.1118/1.4948498
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发表时间：
2016-06-01
期刊：
影响因子：
3.8
通讯作者：
Tamaki, Tsuneo
中科院分区：
文献类型：
--
作者：
Teramoto, Atsushi;Fujita, Hiroshi;Tamaki, Tsuneo
目的：利用正电子发射断层扫描(PET)和计算机断层扫描(CT)图像对孤立性肺结节进行自动检测具有良好的敏感性，但难以发现与正常器官接触的结节，需要进一步努力才能进一步减少假阳性(FP)。提出了一种利用卷积神经网络(CNN)对PET/CT图像中的肺结节进行FP-Reduction检测的改进方法。在CT图像中，首先使用活动轮廓过滤器来检测大块区域，活动轮廓过滤器是一种具有可变形核形状的对比度增强过滤器。随后，将由PET图像检测到的高摄取区与由CT图像检测到的区域合并。利用集成方法剔除FP候选图像；该方法包括两个特征提取，一个是通过形状/代谢特征分析，一个是通过CNN，然后是一个两步分类器，一个步骤是基于规则，另一个步骤是基于支持向量机。结果：作者使用癌症筛查程序收集的104幅PET/CT图像来评估检测性能。早期发现候选抗体的敏感度为97.2%，平均72.8个/例。改进后的FP-Reduction方法检测灵敏度为90.1%，平均每例检测4.9个FP，几乎消除了已有研究中FP的一半。结论：提出了一种改进的基于CNN技术的FP-Reduction方案，用于检测PET/CT图像中的肺结节。作者的集成FP-Reducing方法消除了93%的FP；他们提出的使用CNN技术的方法消除了先前研究中存在的大约一半的FP。这些结果表明，他们的方法在利用PET/CT图像对肺结节进行计算机辅助检测方面可能是有用的。(C)2016年作者(S)。
Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs).Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using an active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines.Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study.Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors' ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images. (C) 2016 Author(s).