Automatic and interactive segmentation of pulmonary lobes and nodules in chest CT images

Abstract

Lung diseases such as lung cancer and chronic obstructive pulmonary disease (COPD) are a main cause of mortality and morbidity. Early detection and determining the right therapy can improve the chances of cure. Although the human eye is very good at qualitatively judging diseases, quantification of disease status and progression is much better done by computers. To be able to perform any automatic quantification, delineation (i.e. segmentation) of the structures of interest in an image is a prerequisite. Lassen has developed algorithms for automatic and interactive segmentation of pulmonary lobes and nodules in CT scans. In challenges and on large sets of publicly available data, the good performance of the methods could be demonstrated. The results were comparable to complete manual segmentations by experts, but with two advantages. First, the proposed approaches are fast and require none or only little user interaction. Second, using the proposed automated methods improves the reproducibility and prevents varying results by different clinicians.