Lung lobe segmentation by anatomy-guided 3D watershed transform

Since the lobes are mostly independent anatomic compartments of the lungs, they play a major role in diagnosis and therapy of lung diseases. The exact localization of the lobe-separating fissures in CT images often represents a non-trivial task even for experts. Therefore, a lung lobe segmentation method suitable to work robustly under clinical conditions must take advantage of additional anatomic information. Due to the absence of larger blood vessels in the vicinity of the fissures, a distance transform performed on a previously generated vessel mask allows a reliable estimation of the boundaries even in cases where the fissures themselves are invisible. To make use of image regions with visible fissures, we linearly combine the original data with the distance map. The segmentation itself is performed on the combined image using an interactive 3D watershed algorithm which allows an iterative refinement of the results. The proposed method was successfully applied to CT scans of 24 patients. Preliminary intraand inter-observer studies conducted for one of the datasets showed a volumetric variability of well below 1%. The achieved structural decomposition of the lungs not only assists in subsequent image processing steps but also allows a more accurate prediction of lobe-specific functional parameters.