Automated segmentation of pulmonary lobes in chest CT scans using evolving surfaces

Segmentation of the pulmonary lobes from chest CT scans is a challenging problem, especially with the presence of incomplete pulmonary fissures. We present an iterative approach for the segmentation of pulmonary lobes via a surface that evolves based on a voxel based fissure confidence function and a smooth prior. The surface is constructed such that it separates the whole lung at all times, and is represented as a height map above a 2D reference plane. A surface evolution process is used to fit the surface to a pulmonary fissure in a scan. At each iteration, the height of all points in the map is adjusted such that the overall confidence is maximized, followed by Laplacian smoothing to enforce a smooth prior on the surface. The proposed method was trained and tuned on 18 CT scans from a clinical trial, and tested on 41 scans of different patients with severe emphysema from another clinical trial. Average overlap ratio of the segmented upper and lower lobes of the left and right lungs are 0.96 and 0.91 respectively. Average overlap ratio for the right middle lobes is 0.86, where minor manual intervention was needed for six cases, and with seven cases excluded because the minor fissure was almost entirely not visible in the CT scan.