Conference proceeding
Liver segment approximation in CT data for surgical resection planning
Proceedings of SPIE, Vol.5370(1), pp.1435-1446
Medical Imaging 2004: Image Processing
05/12/2004
DOI: 10.1117/12.535514
Abstract
Surgical planning of liver tumor resections requires detailed three-dimensional (3D) understanding of the complex arrangement of vasculature, liver segments and tumors. Knowledge about location and sizes of liver segments is important for choosing an optimal surgical resection approach and predicting postoperative residual liver capacity. The aim of this work is to facilitate such surgical planning process by developing a robust method for portal vein tree segmentation. The work also investigates the impact of vessel segmentation on the approximation of liver segment volumes. For segment approximation, smaller portal vein branches are of importance. Small branches, however, are difficult to segment due to noise and partial volume effects. Our vessel segmentation is based on the original gray-values and on the result of a vessel enhancement filter. Validation of the developed portal vein segmentation method in computer generated phantoms shows that, compared to a conventional approach, more vessel branches can be segmented. Experiments with in vivo acquired liver CT data sets confirmed this result. The outcome of a Nearest Neighbor liver segment approximation method applied to phantom data demonstrates, that the proposed vessel segmentation approach translates into a more accurate segment partitioning.
Details
- Title: Subtitle
- Liver segment approximation in CT data for surgical resection planning
- Creators
- Reinhard Beichel - University of GrazThomas Pock - University of GrazChristian Janko - University of GrazRoman B Zotter - University of GrazBernhard Reitinger - University of GrazAlexander Bornik - University of GrazKalman Palagyi - University of SzegedErich Sorantin - Univ. Hospital Graz (Austria)Georg Werkgartner - Univ. Hospital Graz (Austria)Horst Bischof - University of GrazMilan Sonka - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of SPIE, Vol.5370(1), pp.1435-1446
- Conference
- Medical Imaging 2004: Image Processing
- DOI
- 10.1117/12.535514
- ISSN
- 0277-786X
- Language
- English
- Date published
- 05/12/2004
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984186590802771
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