IEEE J Biomed Health Inform. 2014 Sep;18(5):1696-706. doi: 10.1109/JBHI.2014.2332955. Epub 2014 Jun 24.
Complete segmentation of diseased lung lobes by automatically identifying fissure surfaces is a nontrivial task, due to incomplete, disrupted, and deformed fissures. In this paper, we present a novel algorithm employing a hybrid two-dimensional/three-dimensional approach for segmenting diseased lung lobes. Our approach models complete fissure surfaces from partial fissures found in individual computed tomography (CT) images. Evaluated using 24 patients' lungs with a variety of different diseases, our algorithm produced root-mean square errors of 2.21 ± 1.21, 2.51 ± 1.36, and 2.38 ± 1.27 mm for segmenting the left oblique fissure (LOF), right oblique fissure (ROF) and right horizontal fissure (RHF), respectively. The average accuracies for segmenting the LOF, ROF, and RHF are 86.59%, 84.80%, and 82.62%, using our ±3-mm percentile measure. These results indicate the feasibility of developing an automatic algorithm for complete segmentation of diseased lung lobes.
由于不完全、破裂和变形的裂隙，自动识别裂隙表面来完成病变肺叶的分割是一项艰巨的任务。在本文中，我们提出了一种新颖的算法，采用二维/三维混合方法来分割病变肺叶。我们的方法从单个 CT 图像中的部分裂隙中构建完整的裂隙表面。使用 24 例具有多种不同疾病的患者的肺部进行评估，我们的算法在分割左斜裂（LOF）、右斜裂（ROF）和右水平裂（RHF）时的均方根误差分别为 2.21±1.21、2.51±1.36 和 2.38±1.27mm。使用我们的±3mm 百分位数测量方法，分割 LOF、ROF 和 RHF 的平均准确率分别为 86.59%、84.80%和 82.62%。这些结果表明，开发一种用于病变肺叶完全分割的自动算法是可行的。
