  Rarely in literature a method of segmentation cares for the edit after the
algorithm delivers. They provide no solution when segmentation goes wrong. We
propose to formulate point distribution model in terms of
centripetal-parameterized Catmull-Rom spline. Such fusion brings interactivity
to model-based segmentation, so that edit is better handled. When the delivered
segment is unsatisfactory, user simply shifts points to vary the curve. We ran
the method on three disparate imaging modalities and achieved an average
overlap of 0.879 for automated lung segmentation on chest radiographs. The edit
afterward improved the average overlap to 0.945, with a minimum of 0.925. The
source code and the demo video are available at http://wp.me/p3vCKy-2S
