Lobe-based quantification of tomographic images is of increasing interest for diagnosis and monitoring lung pathology. With modern tomography scanners providing data sets with hundreds of slices, manual segmentation is time-consuming and not feasible in the clinical routine. Especially for patients with severe lung pathology that are of particular clinical importance, automatic segmentation approaches frequently generate partially inaccurate or even completely unacceptable results. In this work we present a modality-independent, semi-automated method that can be used both for generic correction of any existing lung lobe segmentation and for segmentation from scratch. Intuitive slice-based drawing of fissure parts is used to introduce user knowledge. Internally, the current fissure is represented as sampling points in 3D space that are interpolated to a fissure surface. Using morphological processing, a 3D impact region is computed for each user-drawn 2D curve. Based on the curve and impact region, the updated lobar boundary surface is immediately computed after each interaction step to provide instant user feedback. The method was evaluated on 25 normal-dose CT scans with a reference standard provided by a human observer. When segmenting from scratch, the average distance to the reference standard was 1.6mm using an average of five interactions and 50 seconds of interaction time per case. When correcting inadequate automatic segmentations, the initial error was reduced from 13.9 to 1.9mm with comparable efforts. The evaluation shows that both correction of a given segmentation and segmentation from scratch can be successfully performed with little interaction in a short amount of time.