Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models
Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models
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DOI：
10.1016/j.compbiomed.2012.09.002
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发表时间：
2012-11-01
影响因子：
7.7
通讯作者：
Raso, G.
中科院分区：
文献类型：
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作者：
Cascio, D.;Magro, R.;Raso, G.
我们提出了一种计算机辅助检测（CAD）系统，该系统可以在螺旋CT扫描中检测到小尺寸（从3mm起）的肺结节。肺结节是肺部的小病变，圆形（实质结节）或蛔虫形（胸膜旁结节）。这两种病变的放射密度都大于肺实质，因此在图像上呈白色。肺结节可能预示着肺癌，早期发现可以提高患者的生存率。CT被认为是结节检测最准确的成像方式。然而，每次检查的大量数据使充分分析变得困难，导致放射科医生遗漏结节。我们开发了一种先进的计算机方法，用于在低剂量和薄层肺CT扫描上自动检测胸膜内和胸膜旁结节。该方法包括初始选择候选结节列表，对每个候选结节进行分割，并对每个分割后的候选结节计算的特征进行分类。提出的CAD系统旨在减少漏诊次数，减少放射科医生的扫描检查时间。我们的系统以相同的方案定位胸膜内和胸膜旁的结节。为了对肺实质进行正确的体积分割，该系统使用区域生长（RG）算法和开放过程来包括胸膜旁结节。利用肺部CAD系统对CT图像中的疑似结节病灶进行分割和提取是一项艰巨的任务。为了解决这一关键问题，我们采用了一种新的三维稳定质量-弹簧模型（MSM），并结合样条曲线重建过程。该模型同时表示特征灰度值范围、有向轮廓信息和形状知识，从而使分割过程更加鲁棒和高效。为了在候选结节中区分真正的结节，应用了一个额外的分类步骤；在此基础上，利用神经网络降低双阈值切割后的误报率。该系统的性能在肺图像数据库联盟（LIDC）提供的84张扫描图上进行了测试，这些扫描图由四位放射科专家注释。系统的检出率为97%，检出率为6.1 FPs/CT。在88%的灵敏度下，降低到2.5 FPs/CT。我们提出了一种新的CT数据集肺结节三维分割技术，使用可变形的msm。结果是一个有效的分割过程，能够收敛，识别一般ROI的形状，经过几次迭代。我们的研究结果表明，使用三维AC模型和基于特征分析的FPs还原过程是一种准确的肺结节分割和分类方法。(C) 2012 Elsevier Ltd.版权所有。
We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate. The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules. (C) 2012 Elsevier Ltd. All rights reserved.