Computerized planning of liver surgery - an overview

Abstract Liver surgery is a field in which computer-based operation planning has an enormous impact on the selection of therapeutic strategy. Based on pre-operative analysis of image data, liver operation planning provides a individual impression of tumor location, the exact structure of the vascular system and an identification of liver segments and sub-segments. In this paper we present an operation planning system that is based on an object-oriented framework. This framework offers extensive automation of the integration process for software modules developed for medical software systems. The operation planning system can calculate the operation proposal results using two different approaches. The first method is based on the Couinaud's classification system, which uses the main stems of the portal and venous trees. The second approach is a portal vein based method. These two approaches were compared using 23 liver CT scans. The volumetric data for individual segments demonstrates differences between the two segment classification methods.

[1]  Adele Goldberg,et al.  SmallTalk 80: The Language , 1989 .

[2]  Carolyn A. Bucholtz,et al.  Shape-based interpolation , 1992, IEEE Computer Graphics and Applications.

[3]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

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

[5]  B. H. McCormick,et al.  Visualization in scientific computing , 1995 .

[6]  Arnold W. M. Smeulders,et al.  Interaction in the segmentation of medical images: A survey , 2001, Medical Image Anal..

[7]  H Delingette,et al.  Virtual reality applied to hepatic surgery simulation: the next revolution. , 1998, Annals of surgery.

[8]  Gerald-P. Glombitza,et al.  Ein interaktives Tool für die Segmenteinteilung der Leber in der chirurgischen Operationsplanung , 1999, Bildverarbeitung für die Medizin.

[9]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[10]  Hans-Peter Meinzer,et al.  User-driven segmentation approach: interactive snakes , 2002, SPIE Medical Imaging.

[11]  Marcus Vetter,et al.  Interaktives Trennen von Gefäßbäumen am Beispiel der Leber , 2001, Bildverarbeitung für die Medizin.

[12]  Hans-Peter Meinzer,et al.  Visualization and attributation of vascular structures for diagnostics and therapy planning. , 2002, Studies in health technology and informatics.

[13]  Ernest M. Stokely,et al.  Medical image segmentation using 3D seeded region growing , 1997, Medical Imaging.

[14]  Hans-Peter Meinzer,et al.  Evaluation of the CHILI Teleradiology Network after 3 years of clinical routine , 2000, EuroPACS.