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.
Proc. SPIE. 5032, Medical Imaging 2003: Image Processing
KEYWORDS: Magnetic resonance imaging, Image segmentation, Image processing, Quantitative analysis, Medical imaging, Computed tomography, In vivo imaging, Algorithm development, Binary data, 3D image processing
Quantitative assessment of tree structures is very important for evaluation of airway or vascular tree morphology and its associated function. Our skeletonization and branch-point identification method provides a basis for tree quantification or tree matching, tree-branch diameter measurement in any orientation, and labeling individual branch segments. All main components of our method were specifically developed to deal with imaging artifacts typically present in volumetric medical image data. The proposed method has been tested in a computer phantom subjected to changes of its orientation as well as in a repeatedly CT-scanned rigid plastic phantom. In all cases, our method produced reliable and well positioned centerlines and branch-points.
Virtual dissection refers to a display technique for polyp detection, where the colon is digitally straightened and then flattened using multirow detector Computed Tomograph (CT) images. As compared to virtual colonoscopy where polyps may be hidden from view behind the folds, the unravelled colon is more suitable for polyp detection, because the entire inner surface of the colon is displayed in a single view. The method was tested both on artificial and cadaveric phantoms. All polyps could be recognized on both phantoms. This technique for virtual dissection requires only a minimum of operator interaction.