School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou 311121, China.
Sensors (Basel). 2022 Nov 7;22(21):8560. doi: 10.3390/s22218560.
The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that incorporates information from the local fissures, the whole lung, and priori pulmonary anatomy knowledge to separate the lobes robustly and accurately. The prior pulmonary atlas is registered to the test CT images with the aid of the detected fissures. The result of the lobe segmentation is obtained by mapping the deformation function on the lobes-annotated atlas. The proposed method is evaluated in a custom dataset with COPD. Twenty-four CT scans randomly selected from the custom dataset were segmented manually and are available to the public. The experiments showed that the average dice coefficients were 0.95, 0.90, 0.97, 0.97, and 0.97, respectively, for the right upper, right middle, right lower, left upper, and left lower lobes. Moreover, the comparison of the performance with a former learning-based segmentation approach suggests that the presented method could achieve comparable segmentation accuracy and behave more robustly in cases with morphological specificity.
肺叶分割在临床评估、病变定位和手术规划中非常重要。自动肺叶分割具有挑战性，主要是由于不完全的裂孔或由肺部疾病引起的形态变化。在这项工作中，我们提出了一种基于学习的方法，该方法结合了局部裂孔、整个肺部和先验肺解剖知识的信息，以稳健和准确地分离肺叶。利用检测到的裂孔，将先验肺图谱配准到测试 CT 图像上。通过在带有肺叶注释的图谱上映射变形函数来获得肺叶分割的结果。该方法在 COPD 定制数据集上进行了评估。从定制数据集随机选择了 24 个 CT 扫描进行手动分割，并向公众开放。实验结果表明，右肺上叶、中叶、下叶、左肺上叶和下叶的平均 Dice 系数分别为 0.95、0.90、0.97、0.97 和 0.97。此外，与以前的基于学习的分割方法的性能比较表明，所提出的方法可以达到相当的分割精度，并且在具有形态特异性的情况下表现更稳健。
