Two-stage Semi-automatic Organ Segmentation Framework using Radial Basis Functions and Level Sets

The automatic segmentation of complex anatomical struc- tures often fails due to low-contrast or missing edges, pathologic alter- ations, or high noise. As an alternative, we propose a novel two-stage semi-automatic algorithm that is able to segment complex structures like the liver shape with moderate user interaction. The first stage of our algorithm is the manual delineation of cross-sections of the anatom- ical structure in 2-D multi-planar reconstruction views. From this set of contours, an initial 3-D surface is reconstructed using radial basis func- tions. In a second step, the surface is evolved using a level set algorithm incorporating a new combination of both image information and shape information, the latter being derived from the initial contours. The al- gorithm has been evaluated for 10 Computed Tomography scans of the liver and has shown promising results.