Constructing smooth nonmanifold meshes of multi-labeled volumetric datasets

This paper presents a method for constructing consistent non-manifold meshes of multi-labeled volumetric datasets. This approach is different to traditional surface reconstruction algorithms which often only support extracting 2-manifold surfaces based on a binary voxel classification. However, in some – especially medical – applications, multi-labeled datasets, where up to eight differently labeled voxels can be adjacent, are subject to visualization resulting in non-manifold meshes. In addition to an efficient surface reconstruction method, a constrained geometric filter is developed which can be applied to these non-manifold meshes without producing ridges at mesh junctions

Liver Segmentation in CT Data: A Segmentation Refinement Approach

Abstract. Liver segmentation is an important prerequisite for planning of surgical interventions like liver tumor resections. For clinical applicability, the segmentation approach must be able to cope with the high variation in shape and gray-value appearance of the liver. In this paper we present a novel segmentation scheme based on a true 3D segmentation refinement concept utilizing a hybrid desktop/virtual reality user interface. The method consists of two main stages. First, an initial segmentation is generated using graph cuts. Second, an interactive segmentation refinement step allows a user to fix arbitrary segmentation errors. We demonstrate the robustness of our method on ten contrast enhanced liver CT scans. Our segmentation approach copes successfully with the high variation found in patient data sets and allows to produce segmentations in a time-efficient manner.