Yao Jianhua, Han Wei, Summers Ronald M
Radiology and Image Sciences Department, Clinical Center, The National Institute of Health, Bethesda, Maryland, 20892.
Proc IEEE Int Symp Biomed Imaging. 2009;2009:241-244. doi: 10.1109/ISBI.2009.5193028.
A pleural effusion is a condition where there is a buildup of abnormal fluid within the pleural space. This paper presents an automated method to evaluate the severity of pleural effusion using regular chest CT images. First the lungs are segmented using region growing, mathematical morphology and anatomical knowledge. Then the visceral and parietal layers of the pleura are extracted based on anatomical landmarks, curve fitting and active contour models. Finally, the pleural space is segmented and the pleural effusion is quantified. Our method was tested on 15 chest CT studies. The automated segmentation is validated against manual tracing and radiologist's qualitative grading. The Pearson correlation between computer evaluation and radiologist's grading is 0.956 (P=10(-7)). The Dice coefficient between the automated and manual segmentation is 0.74±0.07, which is comparable to the variation between two different manual tracings.
胸腔积液是指胸腔内积聚异常液体的一种病症。本文提出了一种利用常规胸部CT图像评估胸腔积液严重程度的自动化方法。首先,使用区域生长、数学形态学和解剖学知识对肺部进行分割。然后,基于解剖标志、曲线拟合和主动轮廓模型提取胸膜的脏层和壁层。最后,对胸腔进行分割并对胸腔积液进行量化。我们的方法在15项胸部CT研究中进行了测试。通过与手动追踪和放射科医生的定性分级进行对比,验证了自动分割的有效性。计算机评估与放射科医生分级之间的Pearson相关性为0.956(P = 10^(-7))。自动分割与手动分割之间的Dice系数为0.74±0.07,这与两种不同手动追踪之间的差异相当。