Retico A, Delogu P, Fantacci M E, Gori I, Preite Martinez A
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Pontecorvo 3, 56127 Pisa, Italy.
Comput Biol Med. 2008 Apr;38(4):525-34. doi: 10.1016/j.compbiomed.2008.02.001. Epub 2008 Mar 14.
A computer-aided detection (CAD) system for the identification of small pulmonary nodules in low-dose and thin-slice CT scans has been developed. The automated procedure for selecting the nodule candidates is mainly based on a filter enhancing spherical-shaped objects. A neural approach based on the classification of each single voxel of a nodule candidate has been purposely developed and implemented to reduce the amount of false-positive findings per scan. The CAD system has been trained to be sensitive to small internal and sub-pleural pulmonary nodules collected in a database of low-dose and thin-slice CT scans. The system performance has been evaluated on a data set of 39 CT containing 75 internal and 27 sub-pleural nodules. The FROC curve obtained on this data set shows high values of sensitivity to lung nodules (80-85% range) at an acceptable level of false positive findings per patient (10-13 FP/scan).
已开发出一种用于在低剂量薄层CT扫描中识别小肺结节的计算机辅助检测（CAD）系统。选择结节候选对象的自动化程序主要基于增强球形物体的滤波器。特意开发并实施了一种基于对结节候选对象的每个体素进行分类的神经方法，以减少每次扫描的假阳性结果数量。该CAD系统已接受训练，对低剂量薄层CT扫描数据库中收集的小的内部和胸膜下肺结节敏感。在包含75个内部结节和27个胸膜下结节的39例CT数据集上对系统性能进行了评估。在此数据集上获得的FROC曲线显示，在每位患者可接受的假阳性结果水平（10 - 13个假阳性/扫描）下，对肺结节具有较高的敏感度（80 - 85%范围）。
