Surface-based interpolation and registration, radiation treatment, and three-dimensional visualization of two-dimensional sliced data from CT or MRT require a precise reconstruction of three-dimensional organ surfaces from two-dimensional segmentation results. Current surface-reconstruction algorithms are based on surface triangulations using heuristics to correlate and connect adjacent object slices. The approaches described in the literature can be divided into triangulations using optimization procedures, Delauny triangulations, and topology-based correlations. All approaches assume a global and invariant vertically oriented correlation strategy that can be applied equally to every organ and every slice. Surface and correlation characteristics vary greatly among bony structures and organs such as the eyes and the brain. An adjusted reconstruction of each organ according to its individual tissue characteristics is necessary to avoid errors in following processing steps such as interpolation, registration, and radiation treatment. To this end, we have designed a model-based surface-reconstruction algorithm that takes individual surface characteristics into account and allows the integration of anatomical knowledge. Three-dimensional surface models are generated from sliced data or any other source of anatomical knowledge. These models are later adjusted to the segmentations, compensating for artifacts and incomplete data.

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