论文信息 - Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model

Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model

We present a fully automatic 3D segmentation method for the liver from contrast-enhanced CT data. It is based on a combination of a constrained free-form and statistical deformable model. The adap- tation of the model to the image data is performed according to a simple model of the typical intensity distribution around the liver boundary and neighboring anatomical structures, considering the potential presence of tumors in the liver. All parameters of the deformation as well as the initial positioning of the model in the data are estimated automatically.

[1] B. Ginneken,et al. 3D Segmentation in the Clinic: A Grand Challenge , 2007 .

[2] Timothy F. Cootes,et al. The Use of Active Shape Models for Locating Structures in Medical Images , 1993, IPMI.

[3] Thomas Lange,et al. A Statistical Shape Model for the Liver , 2002, MICCAI.

[4] H. Lamecker,et al. Segmentation of the Liver using a 3D Statistical Shape Model , 2004 .

[5] Max A. Viergever,et al. Efficient and reliable schemes for nonlinear diffusion filtering , 1998, IEEE Trans. Image Process..

[6] Hans-Peter Meinzer,et al. A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation , 2007, IPMI.

[7] Jürgen Weese,et al. Shape Constrained Deformable Models for 3D Medical Image Segmentation , 2001, IPMI.

[8] Hans-Peter Meinzer,et al. Active Shape Models for a Fully Automated 3D Segmentation of the Liver - An Evaluation on Clinical Data , 2006, MICCAI.

[9] Johan Montagnat,et al. Volumetric medical images segmentation using shape constrained deformable models , 1997, CVRMed.