Automatic Segmentation of the Liver for Preoperative Planning of Resections

Abstract

This work presents first quantitative results of a method for automatic liver segmentation from CT data. It is based on a 3D deformable model approach using a-priori statistical information about the shape of the liver gained from a training set. The model is adapted to the data in an iterative process by analysis of the grey value profiles along its surface normals after nonlinear diffusion filtering. Leave-one-out experiments over 26 CT data sets reveal an accuracy of 2.4 mm with respect to the manual segmentation.

Hans Lamecker

Director, Software Development

Advancing 3D analysis, planning, design and manufacturing using innovative computational methods and tools