We present a fast and robust approach for automatic centerline extraction of tubular structures. The underlying idea is to cut traditional snakes into a set of shorter, independent segments - so-called snakelets. Following the same variational principles, each snakelet acts locally and extracts a subpart of the overall structure. After a parallel optimization step, outliers are detected and the remaining segments then form an implicit centerline. No manual initialization of the snakelets is necessary, which represents one advantage of the method. Moreover, computational complexity does not directly depend on dataset size, but on the number of snake segments necessary to cover the structure of interest, resulting in short computation times. Lastly, the approach is robust even for very complex datasets such as the small intestine. Our approach was tested on several medical datasets (CT datasets of colon, small bowel, and blood vessels) and yielded smooth, connected centerlines with few or no branches. The computation time needed is less than a minute using standard computing hardware.