Segmentation of Lung Lobes in High-Resolution Isotropic CT Images

Abstract

Modern multislice computed tomography (CT) scanners produce isotropic CT images with a thickness of 0.6 mm. These CT images offer detailed information of lung cavities, which could be used for better surgical planning of treating lung cancer. The major challenge for developing a surgical planning system is the automatic segmentation of lung lobes by identifying the lobar fissures. This paper presents a lobe segmentation algorithm that uses a two-stage approach: 1) adaptive fissure sweeping to find fissure regions and 2) wavelet transform to identify the fissure locations and curvatures within these regions. Tested on isotropic CT image stacks from nine anonymous patients with pathological lungs, the algorithm yielded an accuracy of 76.7%-94.8% with strict evaluation criteria. In comparison, surgeons obtain an accuracy of 80% for localizing the fissure regions in clinical CT images with a thickness of 2.5-7.0 mm. As well, this paper describes a procedure for visualizing lung lobes in three dimensions using software--amira--and the segmentation algorithm. The procedure, including the segmentation, needed about 5 min for each patient. These results provide promising potential for developing an automatic algorithm to segment lung lobes for surgical planning of treating lung cancer.