We present an automatic lung lobe segmentation algorithm for COPD patients. The method enhances fissures,
removes unlikely fissure candidates, after which a B-spline is fitted iteratively through the remaining candidate
objects. The iterative fitting approach circumvents the need to classify each object as being part of the fissure
or being noise, and allows the fissure to be detected in multiple disconnected parts. This property is beneficial
for good performance in patient data, containing incomplete and disease-affected fissures.
The proposed algorithm is tested on 22 COPD patients, resulting in accurate lobe-based densitometry, and a
median overlap of the fissure (defined 3 voxels wide) with an expert ground truth of 0.65, 0.54 and 0.44 for the
three main fissures. This compares to complete lobe overlaps of 0.99, 0.98, 0.98, 0.97 and 0.87 for the five main
lobes, showing promise for lobe segmentation on data of patients with moderate to severe COPD.