Graduate School of Information Science, Nagoya University, Nagoya, Japan.
Med Image Anal. 2012 Jan;16(1):63-74. doi: 10.1016/j.media.2011.05.005. Epub 2011 May 19.
One important aspect of lung cancer staging is the assessment of mediastinal lymph nodes in 3-D chest computed tomography (CT) images. In the current clinical routine this is done manually by analyzing the 3-D CT image slice by slice to find nodes, evaluate them quantitatively, and assign labels to them for describing the clinical and pathologic extent of metastases. In this paper we present a method to automate the process of lymph node detection and labeling by creation of a mediastinal average image and a novel lymph node atlas containing probability maps for mediastinal, aortic, and N1 nodes. Utilizing a fast deformable registration approach to match the atlas with CT images of new patients, our method can maintain an acceptable runtime. In comparison to previously published methods for mediastinal lymph node detection and labeling it also shows a good sensitivity and positive predictive value.
肺癌分期的一个重要方面是评估三维胸部 CT（CT）图像中的纵隔淋巴结。在当前的临床常规中，这是通过对 3-D CT 图像进行逐片分析来手动完成的，以找到淋巴结，对其进行定量评估，并对其进行标记，以描述转移的临床和病理范围。在本文中，我们提出了一种通过创建纵隔平均图像和包含纵隔、主动脉和 N1 节点概率图的新淋巴结图谱来自动完成淋巴结检测和标记过程的方法。利用快速变形配准方法将图谱与新患者的 CT 图像进行匹配，我们的方法可以保持可接受的运行时间。与以前发表的用于纵隔淋巴结检测和标记的方法相比，它还显示出良好的灵敏度和阳性预测值。
