Paper
17 March 2008 Development of preoperative liver and vascular system segmentation and modeling tool for image-guided surgery and surgical planning
Senhu Li, Jonathan M. Waite, Brian T. Lennon, James D. Stefansic, Rui Li, Benoit M. Dawant
Author Affiliations +
Abstract
Interactive image-guided liver surgery (Linasys device, Pathfinder Therapeutics, Inc., Nashville, TN) requires a user-oriented, easy-to-use, fast segmentation preoperative surgical planning system. This system needs to build liver models displaying the liver surface, tumors, and the vascular system of the liver. A robust and efficient tool for this purpose was developed and evaluated. For the liver surface or other bulk shape organ segmentation, the delineation was conducted on multiple slices of a CT image volume with a region growing algorithm. This algorithm incorporates both spatial and temporal information of a propagating front to advance the segmenting contour. The user can reduce the number of delineation slices during the processing by using interpolation. When comparing our liver segmentation results to those from MeVis (Breman, Germany), the average overlap percentage was 94.6%. For portal and hepatic vein segmentation, three-dimensional region growing based on image intensity was used. All second generation branches can be identified without time-consuming image filtering and manual editing. The two veins are separated by using mutually exclusive region growing. The tool can be used to conduct segmentation and modeling of the liver, veins, and other organs and can prepare image data for export to Linasys within one hour.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Senhu Li, Jonathan M. Waite, Brian T. Lennon, James D. Stefansic, Rui Li, and Benoit M. Dawant "Development of preoperative liver and vascular system segmentation and modeling tool for image-guided surgery and surgical planning", Proc. SPIE 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, 69180C (17 March 2008); https://doi.org/10.1117/12.772821
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Image segmentation

Liver

Surgery

Veins

3D modeling

Tumors

Data modeling

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