TY - GEN A1 - Lamecker, Hans A1 - Lange, Thomas A1 - Seebass, Martin T1 - Segmentation of the Liver using a 3D Statistical Shape Model N2 - This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between different surfaces. We propose to use a geometric approach that is based on minimizing the distortion of the correspondence mapping between two different surfaces. For the adaption of the shape model to the image data a profile model based on the grey value appearance of the liver and its surrounding tissues in contrast enhanced CT data was developed. The robustness of this method results from a previous nonlinear diffusion filtering of the image data. Special focus is turned to the quantitative evaluation of the segmentation process. Several different error measures are discussed and implemented in a study involving more than 30 livers. T3 - ZIB-Report - 04-09 KW - Statistical Shape Model KW - 3D Correspondence Problem KW - Model-Based Segmentation KW - Diffusion Filtering KW - Geometric Distortion KW - Surface Distance Y1 - 2004 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/784 UR - https://nbn-resolving.org/urn:nbn:de:0297-zib-7847 ER -