Sensakovic William F, Armato Samuel G, Starkey Adam, Caligiuri Philip
Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA.
Med Phys. 2006 Sep;33(9):3085-93. doi: 10.1118/1.2214165.
Segmentation of the lungs within magnetic resonance (MR) scans is a necessary step in the computer-based analysis of thoracic MR images. This process is often confounded by image acquisition artifacts and disease-induced morphological deformation. We have developed an automated method for lung segmentation that is insensitive to these complications. The automated method was applied to 23 thoracic MR scans (413 sections) obtained from 10 patients. Two radiologists manually outlined the lung regions in a random sample of 101 sections (n=202 lungs), and the extent to which disease or artifact confounded lung border visualization was evaluated. Accuracy of lung regions extracted by the automated segmentation method was quantified by comparison with the radiologist-defined lung regions using an area overlap measure (AOM) that ranged from 0 (disjoint lung regions) to 1 (complete overlap). The AOM between each observer and the automated method was 0.82 when averaged over all lungs. The average AOM in the lung bases, where lung segmentation is most difficult, was 0.73.
在基于计算机的胸部磁共振（MR）图像分析中，对肺部进行分割是一个必要步骤。这个过程常常受到图像采集伪影和疾病引起的形态变形的干扰。我们开发了一种对这些并发症不敏感的肺部自动分割方法。该自动方法应用于从10名患者身上获取的23例胸部MR扫描（共413个切片）。两名放射科医生在101个切片（n = 202个肺）的随机样本中手动勾勒出肺部区域，并评估疾病或伪影对肺边界可视化的干扰程度。通过使用范围从0（不相交的肺部区域）到1（完全重叠）的面积重叠测量（AOM），将自动分割方法提取的肺部区域的准确性与放射科医生定义的肺部区域进行比较来进行量化。当对所有肺部进行平均时，每个观察者与自动方法之间的AOM为0.82。在肺部最难以分割的肺底部，平均AOM为0.73。
