Fast fully-automated model-driven liver segmentation utilizing slicewise applied levelsets on large CT data

Gerald Adam Zwettler, Werner Backfrieder, Roland Swoboda, Franz Pfeifer

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

11 Citations (Scopus)

Abstract

Modern Computed Tomography scans are acquired down to a slice thickness of 0.5 mm thus yielding a huge number of 2D slices to be examined by the physician. Hence the need for automated computer assisted diagnostics, e.g. in the field of abdominal scans for liver tumor diagnostics and surgery planning, arises. In this work a fully-automated algorithm for robust and accurate segmentation of the liver parenchyma, a prerequisite for liver lobe classification and resection planning, is presented. A first estimate for liver segmentation is achieved by applying a normalized liver model to the CT data. Based on this pre-segmentation parameters for level set segmentation on a slice-by-slice strategy are assessed, thus enabling a fully-automated segmentation of the liver parenchyma. The slice-by-slice level set propagation utilizes fast-marching and threshold level set implementations.

Original languageEnglish
Title of host publicationProceedings
PublisherDIPTEM University of Genova
Pages161-166
Publication statusPublished - 2009
Event21st European Modeling and Simulation Symposium, EMSS 2009 - Puerto de la Cruz, Spain
Duration: 23 Sep 200925 Sep 2009

Conference

Conference21st European Modeling and Simulation Symposium, EMSS 2009
CountrySpain
CityPuerto de la Cruz
Period23.09.200925.09.2009

Keywords

  • Fast-marching level set
  • Fully-automated CT data segmentation
  • Model-driven liver segmentation
  • Threshold level set

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