Shape-Based Registration of Kidneys Across Differently Contrasted CT Scans

We present a method to register kidneys from Computed Tomography (CT) scans with and without contrast enhancement. The method builds a patient-specific kidney shape model from the contrast enhanced image, and then matches it against automatically segmented candidate surfaces extracted from the pre-contrast image to find the alignment. Only the object of interest is used to drive the alignment, providing results that are robust to near-rigid relative motions of the kidney with respect to the surrounding tissues. Shape-based features are used, as opposed to intensity-based ones, and consequently the resulting registration is invariant to the inherent contrast variations. The contributions of this work are: a surface grouping and segmentation algorithm driven by smooth curvature constraints, and a framework to register image volumes under contrast variation, relative motion and local deformation with minimal user intervention. Encouraging experimental results with real patient images, all with various kinds and sizes of kidney lesions, validate the approach.