Multi-layer deformable models for medical image segmentation

In this work, a Multi-Layer Deformable Model (MLDM) for medical image segmentation is proposed. In contrast to common deformable model based segmentation approaches our new method incorporates a multi-layer geometric model that allows a sampling of the organ's interior. An adaptation logic processes the additional information gained from interior layers in order to fit the model to the data. The deformation is coupled with a dynamic internal energy function represented by a link-oriented flexibility in order to allow the model to accurately adapt to cavities. Exploiting the additional depth information, our approach detects low contrasted transitions between organs more reliably and recovers better from bad model initialization than existing methods. Our approach has been evaluated using representative CT data sets of the liver as well as CT bladder scans. Evaluation using ground truth data showed that our multi-layer technique yields superior results in contrast to common single surface segmentation. Since the amount of layers is flexible, the most interior regions which only carry little regional information can be excluded from optimization. Together with the linear nature of MLDM optimization our approach outperforms other volumetric segmentation methods in terms of speed.