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Automatic Segmentation of the Liver for Preoperative Planning of Resections
 

Summary: Automatic Segmentation of the Liver for
Preoperative Planning of Resections
Hans Lamecker, Thomas Lange, Martin Seebaß,
Sebastian Eulenstein, Malte Westerhoff, Hans­Christian Hege
Zuse­Institute­Berlin (ZIB),
Takustr. 7, 14195 Berlin, Germany
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. Leave­one­out
experiments over 26 CT data sets reveal an accuracy of 2.4 mm with respect to the
manual segmentation.
1 Introduction
Individual preoperative surgical planning for resections of tumors in the liver requires seg-
mentation of the liver tissue [1]. Reliable image segmentation is essential for the correct
prediction of the blood circulation regions.
Semi-automatic methods may reduce the user interaction time for segmentation. However in
the clinical routine automatic methods are desirable. 3D statistical shape models are promis-
ing for robust and automatic segmentation of medical images.

  

Source: Andrzejak, Artur - Konrad-Zuse-Zentrum für Informationstechnik Berlin

 

Collections: Computer Technologies and Information Sciences