Preoperative planning combined with image-guidance has shown promise towards increasing the accuracy of liver
resection procedures. The purpose of this study was to validate one such preoperative planning tool for four patients
undergoing hepatic resection. Preoperative computed tomography (CT) images acquired before surgery were used to
identify tumor margins and to plan the surgical approach for resection of these tumors. Surgery was then performed
with intraoperative digitization data acquire by an FDA approved image-guided liver surgery system (Pathfinder
Therapeutics, Inc., Nashville, TN). Within 5-7 days after surgery, post-operative CT image volumes were acquired.
Registration of data within a common coordinate reference was achieved and preoperative plans were compared to
the postoperative volumes. Semi-quantitative comparisons are presented in this work and preliminary results indicate
that significant liver regeneration/hypertrophy in the postoperative CT images may be present post-operatively. This
could challenge pre/post operative CT volume change comparisons as a means to evaluate the accuracy of
preoperative surgical plans.
KEYWORDS: Image segmentation, Liver, Surgery, Veins, 3D modeling, Tumors, Data modeling, Image-guided intervention, Image processing algorithms and systems, Systems modeling
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.
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