We present real-time vascular visualization methods, which extend on illustrative rendering techniques to particularly accentuate spatial depth and to improve the perceptive separation of important vascular properties such as branching level and supply area. The resulting visualization can and has already been used for direct projection on a patient's organ in the operation theater where the varying absorption and reflection characteristics of the surface limit the use of color. The important contributions of our work are a GPU-based hatching algorithm for complex tubular structures that emphasizes shape and depth as well as GPU-accelerated shadow-like depth indicators, which enable reliable comparisons of depth distances in a static monoscopic 3D visualization. In addition, we verify the expressiveness of our illustration methods in a large, quantitative study with 160 subjects.
Abstract. We introduce a novel technique that allows for an automatic quantification of MR DTI parameters along arbitrarily oriented fiber bundles. Most previous methods require either a manual placement of ROIs, are limited to single fiber tracts, or are limited to bundles which are perpendicular to one of the three image planes. Thus, the quantification process is made much more time-efficient and robust by our new approach. We compare our technique with a manual quantification of an expert and show the similarity of the results. Furthermore, we demonstrate how to visualize the parameters at a certain position of the fiber bundle so that areas of interest can easily be examined.
Automated lung lobe segmentation methods often fail for challenging and clinically relevant cases with incomplete fissures or substantial amounts of pathology. We present a fast and intuitive method to interactively correct a given lung lobe segmentation or to quickly create a lobe segmentation from scratch based on a lung mask. A given lobar boundary is converted into a mesh by principal component analysis of 3D lobar boundary markers to obtain a plane where nodes correspond to the position of the markers. An observer can modify the mesh by drawing on 2D slices in arbitrary orientations. After each drawing, the mesh is immediately adapted in a 3D region around the user interaction. For evaluation we participated in the international lung lobe segmentation challenge LObe and lung analysis 2011 (LOLA11). Two observers applied the method to correct a given lung lobe segmentation obtained by a fully automatic method for all 55 CT scans of LOLA11. On average observer 1/2 required 8 ± 4/25 ± 12 interactions per case and took 1:30 ± 0:34/3:19 ± 1:29 min. The average distances to the reference segmentation were improved from an initial 2.68 ± 14.71 mm to 0.89 ± 1.63/0.74 ± 1.51 mm. In addition, one observer applied the proposed method to create a segmentation from scratch. This took 3:44 ± 0:58 minutes on average per case, applying an average of 20 ± 3 interactions to reach an average distance to the reference of 0.77 ± 1.14 mm. Thus, both the interactive corrections and the creation of a segmentation from scratch were feasible in a short time with excellent results and only little interaction. Since the mesh adaptation is independent of image features, the method can successfully handle patients with severe pathologies, provided that the human operator is capable of correctly indicating the lobar boundaries.
We introduce novel data structures and algorithms for clustering white matter fiber tracts to improve accuracy and robustness of existing techniques. Our novel fiber grid combined with a new randomized soft-division algorithm allows for defining the fiber similarity more precisely and efficiently than a feature space. A fine-tuning of several parameters to a particular fiber set -as it is often required if using a feature space -becomes obsolete. The idea is to utilize a 3D grid where each fiber point is assigned to cells with a certain weight. From this grid, an affinity matrix representing the fiber similarity can be calculated very efficiently in time O(n) in the average case, where n denotes the number of fibers. This is superior to feature space methods which need O(n 2 ) time. Our novel eigenvalue regression is capable of determining a reasonable number of clusters as it accounts for inter-cluster connectivity. It performs a linear regression of the eigenvalues of the affinity matrix to find the point of maximum curvature in a list of descending order. This allows for identifying inner clusters within coarse structures, which automatically and drastically reduces the a-priori knowledge required for achieving plausible clustering results. Our extended multiple eigenvector clustering exhibits a drastically improved robustness compared to the wellknown elongated clustering, which also includes an automatic detection of the number of clusters. We present several examples of artificial and real fiber sets clustered by our approach to support the clinical suitability and robustness of the proposed techniques.
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