Running projects

Financed by others

  • GV-AP1

    Discrete geometric structures motivated by applications in architecture

    Prof. Alexander I. Bobenko

    Project heads: Prof. Alexander I. Bobenko
    Project members: -
    Duration: 01.07.2012 - 30.06.2020
    Status: running
    Located at: Technische Universität Berlin

    Description

    Many of today's most striking buildings are nontraditional freeform shapes. Their fabrication is a big challenge, but also a rich source of research topics in geometry. Project A08 addresses key questions such as: "How can we most efficiently represent and explore the variety of manufacturable designs?" or "Can we do this even under structural constraints such as force equilibrium?" Answers to these questions are expected to support the development of next generation modelling tools which combine shape design with key aspects of function and fabrication.

    http://www.discretization.de/en/projects/C01/
  • GV-AP5

    Geometric Constraints for Polytopes

    Raman Sanyal / Prof. Günter M. Ziegler

    Project heads: Raman Sanyal / Prof. Günter M. Ziegler
    Project members: -
    Duration: 01.07.2012 - 30.06.2020
    Status: running
    Located at: Freie Universität Berlin

    Description

    Polytopes are solid bodies bounded by flat facets. Alternatively, they can be described as the convex hull of their vertices. Thus a polytope can be presented by information on two aspects, a geometric one: "What are the coordinates of the vertices" and a combinatorial one: "Which vertex is incident to which face". There are many interrelations between these two levels: Combinatorial requirements enforce restrictions on the geometry, and vice versa. A03 studies aspects of this interplay.

    http://www.discretization.de/en/projects/A03/
  • GV-AP8

    In vivo and in silico analyses in humans: Cartilage loading of patients' individual knees - the role of soft tissue structures

    Hon Prof. Hans-Christian Hege / Dr. Martin Weiser

    Project heads: Hon Prof. Hans-Christian Hege / Dr. Martin Weiser
    Project members: -
    Duration: 01.10.2014 - 30.09.2018
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    While a relationship between knee joint laxity and osteoarthritis is often assumed, the exact mechanism is not yet fully understood. It is not clear how stabilization by either the cross ligaments or muscle forces affect the local cartilage stress and strain. We develop a comprehensive analysis tool for individual patients. On one hand, we couple a dynamic multibody model to a quasistatic contact solver for the cartilage and validate it against in vivo measurement data from patient groups at Charite. On the other hand, we develop visualization and statistical analysis tools that allow to understand the impact of anatomical variation and cross ligament loss on the mechanical loading of cartilage and correlate this to osteoarthritis progression.

    http://www.zib.de/projects/vivo-and-silico-analyses-humans-cartilage-loading-patients%E2%80%99-individual-knees-%E2%80%93-role-soft-tissue
  • GV-AP9

    Analysis and quantification of morphological and structural changes in cartilage

    Dr.-Ing. Stefan Zachow

    Project heads: Dr.-Ing. Stefan Zachow
    Project members: -
    Duration: 01.10.2014 - 30.09.2018
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    Within the PrevOP research network "Preventing the progression of primary Osteoarthritis by high impact long-term Physical exercise regimen – key mechanisms, efficacy, and long-term results." the aim of sub-project 4 is to assess and to quantitatively analyse morphological and structural changes in cartilage with respect to different levels of exercise to support the hypothesis that cartilage competence is maintained through muscle strengthening. It is assumed that morphology and structure of cartilage and muscle as well as progression of osteoarthritis can be quantitatively assessed with medical imaging techniques. The proposed work program is focussed on monitoring and analysis of changes in cartilage volume, shape, and quality - based on different but combined medical imaging modalities - and its relation to existing OA scores.

    http://www.zib.de/projects/analysis-and-quantification-morphological-and-structural-changes-cartilage
  • GV-AP11

    Transfer of research prototypes to the commercial visualization systems Amira and Avizo

    Dr. Steffen Prohaska

    Project heads: Dr. Steffen Prohaska
    Project members: -
    Duration: 01.09.2012 - 31.12.2017
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    Amira and Avizo are professional software products for 3D visualization, geometry reconstruction and data analysis. The software has been designed and developed at the Zuse Institute Berlin (ZIB) in the department of Visualization and Data Analysis. Today, Amira and Avizo are jointly developed by the ZIB and the FEI Visualization Sciences Group in Bordeaux, France. The goal of the Amira and Avizo Technology Transfer project is to speed up the integration of new algorithms developed at ZIB into the commercial versions of Amira and Avizo. FEI benefits from this project by an early integration of state of the art research into the commercial software. ZIB benefits from the technology transfer in two important ways: Customer support for commercially available modules is provided by the companies; and research prototypes are improved and maintained after the end of a research project, making it easier for other researchers to build upon them in the future.

    http://www.zib.de/projects/transfer-research-prototypes-commercial-visualization-systems-amira-and-avizo
  • GV-AP13

    Low-Dimensional Models for Complex Structured Data

    Prof. Dr. Gitta Kutyniok

    Project heads: Prof. Dr. Gitta Kutyniok
    Project members: -
    Duration: 01.10.2015 - 30.09.2018
    Status: running
    Located at: Technische Universität Berlin

    Description

    DEDALE is an interdisciplinary project that intends to develop the next generation of data analysis methods for the new era of big data in astrophysics and compressed sensing. Novel data analysis methods in machine learning allow for a better preservation of the intrinsic physical properties of real data that generally live on intricate spaces, such as signal manifolds.

    Our project have three main scientific directions:
    • Introduce new models and methods to analyse and restore complex, multivariate, manifold-based signals.
    • Exploit the current knowledge in optimisation and operations research to build efficient numerical data processing algorithms in the large-scale settings.
    • Show the reliability of the proposed methods in two different applications: one in cosmology and one in remote sensing.


    http://dedale.cosmostat.org/
  • GV-AP14

    Modeling Synaptic Connectivity in Anatomically Realistic Neural Networks

    Hon Prof. Hans-Christian Hege

    Project heads: Hon Prof. Hans-Christian Hege
    Project members: -
    Duration: 01.01.2016 - 31.12.2017
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    The goal of the NeuroConnect project is
    • to generate anatomically realistic 3D neural network models,
    • to provide tools to analyze such models and
    • to extract information for numerical simulations of neural activity, particularly the synaptic connectivity.

    This requires the development of new methods to effectively specify, visualize, and quantify the information of interest in these potentially large (>500k neurons) and complex neural networks, as well as efficient data structures to represent and process this data. Methods to extract the anatomical data underlying the network model and the modeling approach have been developed in the past Cortex In Silico project.

    http://www.zib.de/projects/modeling-synaptic-connectivity-anatomically-realistic-neural-networks
  • GV-AP15

    Geometrical and topological microstructure analysis of metal and steel grains

    PD Dr. Frank Lutz / Prof. Dr. Boris Springborn

    Project heads: PD Dr. Frank Lutz / Prof. Dr. Boris Springborn
    Project members: -
    Duration: 01.03.2016 - 28.02.2018
    Status: running
    Located at: Technische Universität Berlin

    Description

    The objective of this project is to develop geometrical and topological approaches to study boundary surfaces of steel grains from voxel data. We plan to use methods from Discrete Differential Geometry and Combinatorial Topology to extract curvature information of grain interfaces in combination with grain topologies.

    http://page.math.tu-berlin.de/~lutz/steel_interfaces/
  • GV-AP16

    Computational and structural aspects of point set surfaces

    Prof. Dr. Konrad Polthier

    Project heads: Prof. Dr. Konrad Polthier
    Project members: Konstantin Poelke / M.Sc. Martin Skrodzki
    Duration: 01.07.2016 - 30.06.2020
    Status: running
    Located at: Freie Universität Berlin

    Description

    In the project “Computational and structural aspects of point set surfaces”, we will develop discrete differential geometric representations for point set surfaces and effective computational algorithms. Instead of first reconstructing a triangle based mesh, our operators act directly on the point set data. The concepts will have contact to meshless methods and ansatz spaces of radial basis functions. As proof of concept of our theoretical investigations we will transfer and implement key algorithms from surface processing, for example, for surface parametrization and for feature aware mesh filtering on point set surfaces. Point set surfaces have a more than 15 year long history in geometry processing and computer graphics as they naturally arise in 3D-data acquisition processes. A guiding principle of these algorithms is the direct processing of raw scanning data without prior meshing – a principle that has a long-established history in classical numerical computations. However, their usage mostly restricts to full dimensional domains embedded in R2 or R3 and a thorough investigation of a differential geometric representation of point set surfaces and their properties is not available. Inspired by the notion of manifolds, we will develop new concepts for meshless charts and atlases. These will be used to implement higher order differential operators including curvature descriptors. On this solid basis of meshless differential operators, we will develop novel algorithms for important geometry processing tasks, such as feature recognition, filtering operations, and surface parameterization.

    http://www.discretization.de/en/projects/C05/
  • GV-AP17

    Machine Learning Approaches for Enhanced, Shape Model Based 3D Image Segmentation

    Dr. Hans Lamecker / Dr.-Ing. Stefan Zachow

    Project heads: Dr. Hans Lamecker / Dr.-Ing. Stefan Zachow
    Project members: Dr. Anirban Mukhopadhyay
    Duration: 01.10.2014 - 30.09.2019
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    Fully automatic segmentation of arbitrary anatomical structures from 3D medical image data is a challenging, yet unsolved problem. Though fully automatic segmentation is essential for further clinical analysis, complexity of anatomical structures across population makes a generalized segmentation scheme extremely challenging. Moreover, specific challenges of different imaging modalities have so far hindered the possibility of a general purpose fully automatic 3D segmentation framework. Statistical 3D shape models have proven to be valuable shape priors that are to be deformed within their range of normal variation in shape to match the respective image information. Within the project, we are aiming to combine Machine Learning along with the statistical shape priors for getting a step closer to a general 3D image segmentation approach. In particular, Machine Learning techniques for image matching based on intensity will be developed in order to improve both the model building as well as the segmentation process. Image-based Cost Functions: Principal Component Analysis (PCA) on local intensity profiles has not proven to beneficially act as a robust cost function. Random Forest Regression Voting (RFRV), though a powerful method for 2D image data, turned out to be impractical for 3D data, due to huge memory consumption and computational time. Dictionary Learning (DL) does not require any heuristics and is general enough to be applied across anatomies and modalities. DL operations are matrix operations, thus being efficiently evaluated. Joint Dictionary Learning: Given 3D image data and accordingly segmented anatomical structures of interest, rotational invariant histograms of oriented gradients (HoG) are sampled at the structures’ boundaries. These feature samples are used as input for learning a dictionary. A second dictionary is learnt for background image information. A combined dictionary of foreground and background features has been established, acting as a cost function for image segmentation. Cost Function for a test patch: Sum of residuals from representations by the two dictionaries.

    http://www.zib.de/projects/machine-learning-approaches-enhanced-shape-model-based-3d-image-segmentation
  • GV-AP18

    TOKMIS – Treating Osteoarthritis in Knee with Mimicked Interpositional Spacer

    Dr.-Ing. Stefan Zachow

    Project heads: Dr.-Ing. Stefan Zachow
    Project members: -
    Duration: 01.03.2015 - 31.01.2019
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    One aim of this project is to analyze a large set of medical image data with respect to the anatomy of the knee joint. The aim is to determine the variation in shape of the knee and knee joint space, respectively bone and cartilage, between distal femur and proximal tibia. Clusters of similar shapes have to be determined in order to design a limited set of knee spacers that fit a wide range of the osteoarthritic population. Data selection: In order to detect different clusters of similar shapes at least 500 MRI datasets need to be processed. The datasets are taken from The Osteoarthritis Initiative (OAI) database. The OAI database contains about 5000 patients. Therefore, a selection has been made based on the Kellgren-Lawrence OA-Score which is available for almost all patients. The Kellgren-Lawrence score differentiates between five grades. For each of the five grades 138 female and 145 male patients have been randomly selected resulting in a preselection of 1415 right knee MRI datasets. In a first step 500 of these datasets are processed and analysed. As MRI protocol SAG_3D_DESS_WE (sagittal 3D dual-echo steady state with selective water excitation) is used for bone and cartilage segmentation. Data processing: Bone and cartilage of distal femur and proximal tibia are segmented automatically using Statistical Shape Models Errors in the automatic segmentation are corrected manually. Additionally, for each knee landmarks of the insertion sites of anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) are placed by hand. Analysis: The aim is to find geometrical clusters in (very) high dimensional data that complicates meaningful clustering. Therefore, a principal component analysis (PCA) is done to reduce the dimensionality. But the PCA is a global technique. Every single point has the same influence on the result. Hence, the idea is to restrict the geometry to a region of interest. Nevertheless, the results still have too many dimensions for clustering. For that reason, correlation and regression analysis between geometry and clinical parameters are done to achieve further reduction of dimensionality.

    http://www.zib.de/projects/treating-osteoarthritis-knee-mimicked-interpositional-spacer