Bellotti R, De Carlo F, Gargano G, Tangaro S, Cascio D, Catanzariti E, Cerello P, Cheran S C, Delogu P, De Mitri I, Fulcheri C, Grosso D, Retico A, Squarcia S, Tommasi E, Golosio Bruno
Dipartimento di Fisica, Università di Bari, Italy.
Med Phys. 2007 Dec;34(12):4901-10. doi: 10.1118/1.2804720.
A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.
本文介绍了一种用于在计算机断层扫描（CT）图像中选择肺结节的计算机辅助检测（CAD）系统。该系统基于区域生长（RG）算法和一种新的主动轮廓模型（ACM），该模型实现了局部凸包，能够绘制肺实质的正确轮廓并包含胸膜结节。CAD系统由三个步骤组成：（1）通过RG算法分割肺实质体积；通过新的ACM技术包含胸膜结节；（2）将RG算法迭代应用于先前分割的体积以检测候选结节；（3）应用双阈值切割和神经网络以减少假阳性（FP）。在临床CT上设置参数后，该系统可对整个扫描进行处理，无需任何手动选择。CT数据库记录于ITALUNG-CT试验的比萨中心，这是意大利首个用于肺癌筛查的随机对照试验。该系统在15次CT扫描（约4700幅断层图像）上对26个结节（15个内部结节和11个胸膜结节）的检测率为88.5%，假阳性率为6.6个/CT。在80%的效率下，假阳性率降至2.47个/CT。
