Murphy K, van Ginneken B, Schilham A M R, de Hoop B J, Gietema H A, Prokop M
Image Sciences Institute, University Medical Center, Utrecht, The Netherlands.
Med Image Anal. 2009 Oct;13(5):757-70. doi: 10.1016/j.media.2009.07.001. Epub 2009 Jul 30.
A scheme for the automatic detection of nodules in thoracic computed tomography scans is presented and extensively evaluated. The algorithm uses the local image features of shape index and curvedness in order to detect candidate structures in the lung volume and applies two successive k-nearest-neighbour classifiers in the reduction of false-positives. The nodule detection system is trained and tested on three databases extracted from a large-scale experimental screening study. The databases are constructed in order to evaluate the algorithm on both randomly chosen screening data as well as data containing higher proportions of nodules requiring follow-up. The system results are extensively evaluated including performance measurements on specific nodule types and sizes within the databases and on lesions which later proved to be malignant. In a random selection of 813 scans from the screening study a sensitivity of 80% with an average 4.2 false-positives per scan is achieved. The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.
本文提出并广泛评估了一种用于胸部计算机断层扫描中结节自动检测的方案。该算法利用形状指数和曲率等局部图像特征来检测肺容积中的候选结构，并应用两个连续的k近邻分类器来减少假阳性。结节检测系统在从大规模实验性筛查研究中提取的三个数据库上进行训练和测试。构建这些数据库是为了在随机选择的筛查数据以及包含更高比例需要随访的结节的数据上评估该算法。对系统结果进行了广泛评估，包括对数据库内特定结节类型和大小以及后来被证明为恶性的病变的性能测量。在从筛查研究中随机选择的813次扫描中，实现了80%的灵敏度，每次扫描平均有4.2个假阳性。所呈现的检测结果是计算机辅助检测（CAD）系统在低剂量筛查研究中性能的实际衡量标准，该研究包括各种大小、类型和纹理的结节。
