We present a novel statistical shape model approach for fully automatic CT liver segmentation. Unlike previous techniques, our method combines learned local shape priors with constraints that are directly derived from the current curvature of the model in order to restrict adaptation to regions where large deformations are expected and observed. Our approach is based on a multi-tiered framework that is more robust against model initialization errors than existing methods, because the model's degrees of freedom are step-wise increased. We evaluated our method on a large data base of 86 CT liver scans from different vendors, protocols, varying resolution and contrast enhancement. For comparison, 50 of the scans were taken from 2 public data bases, one of it being the MICCAI'07 liver segmentation challenge data base. Evaluation shows state of the art results with an average mean surface distance between 1.3 mm and 1.85 mm compared to ground truth depending on the image resolution. With an average segmentation time of 45 seconds our approach outperforms other automatic methods.
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