An Enhanced Framework for Automated Segmentation of the Pulmonary Lobes from Chest CT Scans using Level Set Approach

Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. Automatic segmentation of the separate human lung lobes is a crucial task in computer aided diagnostics and intervention planning, and required for example for determination of disease spreading or pulmonary parenchyma quantification. In this work, a novel approach for lobe segmentation based on multi-region level sets is presented. In a first step, interlobular fissures are detected using a supervised enhancement filter. The fissures are then used to compute a cost image, which is incorporated in the level set approach. By this, the segmentation is drawn to the fissures at places where structure information is present in the image. In areas with incomplete fissures (e.g. due to insufficient image quality or anatomical conditions) the smoothing term of the level sets applies and a closed continuation of the fissures is provided. The approach is tested on nine pulmonary CT scans. Lobe segmentation can be a very challenging task if images lack in quality or if anatomical anomalies occur. Using level sets for image segmentation has many advantages. First of all, level sets yield a nice representation of regions and their boundaries on the pixel grid without the need of complex data structures. This considerably simplifies optimization, as variational methods and standard numerics can be employed. Furthermore, level sets can describe topological changes in the segmentation, i.e. parts of a region can split and merge.