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.
Planning of surgical liver tumor resections based on image data from X-ray computed tomography requires
correct segmentation of the liver, liver vasculature and pathological structures. Automatic liver segmentation
methods frequently fail in cases where the anatomy is degenerated by lesions or other present liver diseases. On
the other hand performing a manual segmentation is a tedious and time consuming task. Therefore Augmented
Reality based segmentation refinement tools are reported, that aid radiologists to efficiently correct incorrect
segmentations in true 3D using head-mounted displays and tracked input devices. The developed methods
facilitate segmentation refinement by interactively deforming a mesh data structure reconstructed from an initial
segmentation. The variety of refinement methods are all accessible through the intuitive, direct 3D user interface
of an Augmented Reality system.
Surgical resection has evolved to an accepted and widely-used method for the treatment of liver tumors. In order
to elaborate an optimal resection strategy, computer-aided planning tools are required. However, measurements
based on 2D cross sectional images are difficult to perform. Moreover, resection planning with current desktopbased
systems using 3D visualization is also a tedious task because of limited 3D interaction. For facilitating the
planning process, different tools are presented allowing easy user interaction in an Augmented Reality environment.
Methods for quantitative analysis like volume calculation and distance measurements are discussed with
focus on the user interaction aspect. In addition, a tool for automatically generating anatomical resection proposals
based on knowledge about tumor locations and the portal vein tree is described. The presented methods
are part of an evolving liver surgery planning system which is currently evaluated by physicians.
Surgical resection of liver tumors requires a detailed three-dimensional understanding of a complex arrangement of vasculature, liver segments and tumors inside the liver. In most cases, surgeons need to develop this understanding by looking at sequences of axial images from modalities like X-ray computed tomography. A system for liver surgery planning is reported that enables physicians to visualize and refine segmented input liver data sets, as well as to simulate and evaluate different resections plans. The system supports surgeons in finding the optimal treatment strategy for each patient and eases the data preparation process. The use of augmented reality contributes to a user-friendly design and simplifies complex interaction with 3D objects. The main function blocks developed so far are: basic augmented reality environment, user interface, rendering, surface reconstruction from segmented volume data sets, surface manipulation and quantitative measurement toolkit. The flexible design allows to add functionality via plug-ins. First practical evaluation steps have shown a good acceptance. Evaluation of the system is ongoing and future feedback from surgeons will be collected and used for design refinements.