van Rikxoort Eva M, de Hoop Bartjan, van de Vorst Saskia, Prokop Mathias, van Ginneken Bram
Image Sciences Institute, Utrecht, The Netherlands.
IEEE Trans Med Imaging. 2009 Apr;28(4):621-30. doi: 10.1109/TMI.2008.2008968. Epub 2009 Feb 10.
Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of computed tomography (CT) scans of the chest. A completely automatic method is presented to segment the lungs, lobes and pulmonary segments from volumetric CT chest scans. The method starts with lung segmentation based on region growing and standard image processing techniques. Next, the pulmonary fissures are extracted by a supervised filter. Subsequently the lung lobes are obtained by voxel classification where the position of voxels in the lung and relative to the fissures are used as features. Finally, each lobe is subdivided in its pulmonary segments by applying another voxel classification that employs features based on the detected fissures and the relative position of voxels in the lobe. The method was evaluated on 100 low-dose CT scans obtained from a lung cancer screening trial and compared to estimates of both interobserver and intraobserver agreement. The method was able to segment the pulmonary segments with high accuracy (77%), comparable to both interobserver and intraobserver accuracy (74% and 80%, respectively).
肺部解剖结构的自动提取为胸部计算机断层扫描（CT）的计算机分析奠定了基础。本文提出了一种完全自动的方法，用于从胸部容积CT扫描中分割出肺、肺叶和肺段。该方法首先基于区域生长和标准图像处理技术进行肺部分割。接下来，通过监督滤波器提取肺裂。随后，通过体素分类获得肺叶，其中将肺内体素的位置以及相对于肺裂的位置用作特征。最后，通过应用另一种体素分类方法，将每个肺叶细分为肺段，该方法利用基于检测到的肺裂和肺叶内体素相对位置的特征。该方法在从肺癌筛查试验中获得的100例低剂量CT扫描上进行了评估，并与观察者间和观察者内一致性的估计值进行了比较。该方法能够以较高的准确率（77%）分割肺段，与观察者间和观察者内的准确率（分别为74%和80%）相当。
