A Hybrid Approach to Segmentation of Diseased Lung Lobes

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.