Segmentation of the Liver using a 3D Statistical Shape Model

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.

[1]  B. O'neill.  Elementary Differential Geometry , 1966 .

[2]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[3]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  Tony DeRose,et al.  Multiresolution analysis of arbitrary meshes , 1995, SIGGRAPH.

[6]  J. E. Jackson,et al.  Statistical Factor Analysis and Related Methods: Theory and Applications , 1995 .

[7]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[8]  Michael S. Floater,et al.  Parametrization and smooth approximation of surface triangulations , 1997, Comput. Aided Geom. Des..

[9]  Paul M. Thompson,et al.  Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic brain atlas based on random vector field transformations , 1997, Medical Image Anal..

[10]  Michael Garland,et al.  Surface simplification using quadric error metrics , 1997, SIGGRAPH.

[11]  Joachim Weickert,et al.  A Review of Nonlinear Diffusion Filtering , 1997, Scale-Space.

[12]  W. Stuetzle,et al.  HIERARCHICAL COMPUTATION OF PL HARMONIC EMBEDDINGS , 1997 .

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

[14]  D Selle,et al.  Segmental anatomy of the liver: poor correlation with CT. , 1998, Radiology.

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

[16]  A. Kelemen,et al.  Three-dimensional model-based segmentation of brain MRI , 1998, Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162).

[17]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[18]  Stéphane Lavallée,et al.  Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery , 1999, Medical Image Anal..

[19]  Joachim Weickert,et al.  Scale-Space Theories in Computer Vision , 1999, Lecture Notes in Computer Science.

[20]  Thomas Lehnert,et al.  Virtual planning of liver resections: image processing, visualization and volumetric evaluation , 1999, Int. J. Medical Informatics.

[21]  James F. O'Brien,et al.  Shape transformation using variational implicit functions , 1999, SIGGRAPH Courses.

[22]  Hans-Christian Hege,et al.  Fast and intuitive generation of geometric shape transitions , 2000, The Visual Computer.

[23]  C. Taylor,et al.  Automated construction of 3D shape models using harmonic maps , 2000 .

[24]  Johan Montagnat,et al.  Fully Automatic Anatomical, Pathological, and Functional Segmentation from CT Scans for Hepatic Surgery , 2000, Medical Imaging: Image Processing.

[25]  Christopher J. Taylor,et al.  A Method of Automated Landmark Generation for Automated 3D PDM Construction , 2000, BMVC.

[26]  Heinz-Otto Peitgen,et al.  Efficient Semiautomatic Segmentation of 3D Objects in Medical Images , 2000, MICCAI.

[27]  Lawrence H. Staib,et al.  Shape-based 3D surface correspondence using geodesics and local geometry , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[28]  A. Pressley.  Elementary Differential Geometry , 2000 .

[29]  Milan Sonka,et al.  Shape- and appearance-based segmentation of volumetric medical images , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[30]  Benoit M. Dawant,et al.  Automatic 3D segmentation of the liver from abdominal CT images: a level-set approach , 2001, SPIE Medical Imaging.

[31]  B. Engquist,et al.  Mathematics Unlimited: 2001 and Beyond , 2001 .

[32]  Timothy F. Cootes,et al.  A minimum description length approach to statistical shape modeling , 2002, IEEE Transactions on Medical Imaging.

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