@phdthesis{Lamecker2008, author = {Lamecker, Hans}, title = {Variational and statistical shape modeling for 3D geometry reconstruction}, year = {2008}, abstract = {The reconstruction of geometric shapes plays an important role in many biomedical applications. One example is the patient-specific, computer-aided planning of complex interventions, which requires the generation of explicitly represented geometric models of anatomical structures from medical image data. Only solutions that require minimal interaction by medical personnel are likely to enter clinical routine. Another example is the planning of surgical corrections of deformities where the target shape is unknown. Surgeons are often forced to resort to subjective criteria. These applications still pose highly challenging reconstruction problems, which are addressed in this thesis. The fundamental hypothesis, pursued in this thesis, is that the problems can be solved by incorporating a-priori knowledge about shape and other application-specific characteristics. Here, we focus mainly on the aspect of geometric shape analysis. The basic idea is to capture the most essential variations of a certain class of geometric objects via statistical shape models, which model typical features contained in a given population, and restrict the outcome of a reconstruction algorithm (more or less) to the space spanned by such models. A fundamental prerequisite for performing statistical shape analysis on a set of different objects is the identification of corresponding points on their associated surfaces. This problem is particularly difficult to solve if the shapes stem from different individuals. The reason lies in the basic difficulty of defining suitable measures of similarity. In this thesis, we divide the correspondence problem into feature and non-feature matching. The feature part depends on the application, while the non-feature part can be characterized by a purely geometric description. We propose two different approaches. The first approach has proved useful in many applications. Yet, it suffers from some practical limitations and does not yield a measure of similarity. Our second, variational, approach is designed to overcome these limitations. In it, we propose to minimize an invariant stretching measure, constrained by previously computed features. An important property, which sets our method apart from previous work, is that it does not require the computation of a global surface parameterization.}, language = {en} } @phdthesis{Dercksen2015, author = {Dercksen, Vincent J.}, title = {Visual computing techniques for the reconstruction and analysis of anatomically realistic neural networks}, year = {2015}, abstract = {To understand how the brain translates sensory input into behavior, one needs to identify, at the cellular level, the involved neural circuitry and the electrical signals it carries. This thesis describes methods and tools that enable neuroscientists to obtain important anatomical data, including neuron numbers and shapes, from 3D microscopy images. On this basis, tools have been developed to create and visually analyze anatomically realistic 3D models of neural networks: 1. An automatic segmentation method for determining the number and location of neuron cell bodies in 3D microscopy images. Application of this method yields a difference of merely ∼4\% between automatically and manually counted cells, which is sufficiently accurate for application in large-scale counting experiments. 2. A method for the automatic alignment of 3D section volumes containing filamentous structures. To this end, an existing point-matching-based method has been adapted such that sections containing neuron and microtubule fragments could be successfully aligned. 3. The Filament Editor, a 3D proof-editing tool for visual verification and correction of automatically traced filaments. The usefulness of the Filament Editor is demonstrated by applying it in a validated neuron reconstruction pipeline to create 3D models of long-range and complex neuronal branches. 4. The tool NeuroNet, which is used to assemble an anatomical model of a neural network representing the rat barrel cortex (or subnetworks therein, e.g. individual cortical columns), based on reconstructed anatomical data, such as neuron distributions and 3D morphologies. The tool estimates synaptic connectivity between neurons based on structural overlap between axons and dendrites. 5. A framework for the interactive visual analysis of synaptic connectivity in such networks at multiple scales. It works from the level of neuron populations down to individual synapse positions on dendritic trees. It comprises the Cortical Column Connectivity Viewer, developed to analyze synaptic connections between neuron populations within and between cortical columns. The usefulness of these methods is demonstrated by applying them to reconstruct and analyze neural networks in the rat barrel cortex. Finally, I describe several applications of these methods and tools by neuroscientists, yielding significant biological findings regarding neuron anatomy and connectivity.}, language = {en} } @misc{KnoetelSeidelProhaskaetal.2017, author = {Kn{\"o}tel, David and Seidel, Ronald and Prohaska, Steffen and Dean, Mason N. and Baum, Daniel}, title = {Automated Segmentation of Complex Patterns in Biological Tissues: Lessons from Stingray Tessellated Cartilage}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65785}, year = {2017}, abstract = {Introduction - Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. Methods - Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. Results - Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.}, language = {en} }