The segmentation of tree-like tubular structures such as coronary arteries and airways is an essential step for many 3D medical imaging applications. Statistical tracking techniques for the extraction of elongated structures have received considerable attention in recent years due to their robustness against image noise and pathological changes. However, most tracking methods are limited to a specific application and do not support branching structures efficiently. In this work, we present a novel statistical tracking approach for the extraction of different types of tubular structures with ringlike cross-sections. Domain-specific knowledge is learned from training data sets and integrated into the tracking process by simple adaption of parameters. In addition, an efficient branching detection algorithm is presented. This approach was evaluated by extracting coronary arteries from 32 CTA data sets and distal airways from 20 CT scans. These data sets were provided by the organizers of the workshop '3D Segmentation in the Clinic: A Grand Challenge II-Coronary Artery Tracking (CAT08)' and 'Extraction of Airways from CT 2009 (EXACT' 09)'. On average, 81.5% overlap and 0.51 mm accuracy for the tracking of coronary arteries were achieved. For the extraction of airway trees, 51.3% of the total tree length, 53.6% of the total number of branches and a 4.98% false positive rate were attained. In both experiments, our approach is comparable to state-of-the-art methods.