Automatic 3D segmentation of the liver from computed tomography images, a discrete deformable model approach

A. Evans, T. Lambrou, A.D. Linney, A. Todd-Pokropek

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

2 Citations (Scopus)

Abstract

Automatic segmentation of the liver has the potential to assist in the diagnosis of disease, preparation for organ transplantation, and possibly assist in treatment planning. This paper presents initial results from work that extends on previous two-dimensional (2D) segmentation methods by implementing full three-dimensional (3D) liver segmentation, using a self-reparameterising discrete deformable model. This method overcomes many of the weaknesses inherent in 2D segmentation techniques, such as the inability to automatically segment separate lobes of the liver in each image slice, and sensitivity to individual-slice noise. Results are presented showing volumetric and overlap comparison of twelve automatically segmented livers with their corresponding manually segmented livers, which were treated as the gold standard for this study
Original languageEnglish
Title of host publication2006 9th International Conference on Control, Automation, Robotics and Vision
PublisherIEEE Explore
Pages1-6
DOIs
Publication statusPublished - 2006
Event9th International Conference on Control, Automation, Robotics and Vision - Singapore, Singapore
Duration: 5 Dec 20067 Dec 2006

Conference

Conference9th International Conference on Control, Automation, Robotics and Vision
Country/TerritorySingapore
Period5/12/067/12/06

Fingerprint

Dive into the research topics of 'Automatic 3D segmentation of the liver from computed tomography images, a discrete deformable model approach'. Together they form a unique fingerprint.

Cite this