Overview

• Application Fields
  • Clinical Research and Health Care
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  • Sustainable Energies
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• Projects
  • Running projects
  • Successfully completed projects
• Publications
• Scientists
• Preprints
• Institutional Cooperation
• Archiv 02-14

Successfully completed projects

• GV-AP2

  Integrating discrete geometries and finite element spaces

  Prof. Dr. Konrad Polthier

  Project heads: Prof. Dr. Konrad Polthier
  Project members: -
  Duration: 01.07.2012 - 30.06.2016
  Status: completed
  Located at: Freie Universität Berlin
  

  Description

  Finite element methods are in every day use in engineering and modelling. The main idea with finite elements is to discretize objects such as machine parts or architectural elements in order to then simulate the movement and behaviour of these objects via discrete computations. Project A04 aims to link experiences from those applications of scientific computing with ideas from discrete geometry to improve the integration of technologies.
  
  http://www.discretization.de/en/projects/A04/

• GV-AP3

  Riemannian manifold learning via shearlet approximation

  Prof. Dr. Gitta Kutyniok

  Project heads: Prof. Dr. Gitta Kutyniok
  Project members: -
  Duration: 01.01.2013 - 30.06.2016
  Status: completed
  Located at: Technische Universität Berlin
  

  Description

  Applied harmonic analysis provides powerful methodologies to approximate geometric objects, which might be given as a Riemannian manifold itself or as an approximating point cloud. The main tools are specifically designed representation systems such as shearlets. These systems are of a multiscale type, thus an approximation process provides different resolution levels. One might ask: "Which resolution level allows detection of which geometric properties, such as curvature or torsion?" Project A10 aims to analyze such relations between approximations and learning of geometrical properties.
  
  http://www.discretization.de/en/projects/A10/

• GV-AP4

  Interactive tools for research and demonstration

  Prof. Dr. Ulrich Pinkall / Prof. John Sullivan

  Project heads: Prof. Dr. Ulrich Pinkall / Prof. John Sullivan
  Project members: -
  Duration: 01.07.2012 - 30.06.2016
  Status: completed
  Located at: Technische Universität Berlin
  

  Description

  Today, software for analyzing and visualizing mathematical objects is an important tool for getting a grip on the mathematical matter one is exploring. There is already a lot of expertise in the SFB/Transregio with software in different fields of differential and combinatorial geometry. Project C01 aims to bring this experience together, for instance by building generic libraries for visualization and 3D-rendering or by enhancing the interoperability of the existing software projects.
  
  http://www.discretization.de/en/projects/C01/

• GV-AP6

  Dynamic Multi-modal Knee Joint Registration for the Analysis of Knee Laxity

  Dr. Rainald Ehrig / Dr.-Ing. Stefan Zachow

  Project heads: Dr. Rainald Ehrig / Dr.-Ing. Stefan Zachow
  Project members: -
  Duration: 01.06.2014 - 31.05.2017
  Status: completed
  Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin
  

  Description

  Changes in limb or joint anatomy, e.g. due to injury or surgery, may lead to functional impairment. Accurate measurement of skeletal kinematics provides the key to understanding the role of joint instabilities on the onset and progression of degenerative diseases. The aim of the project is to measure knee joint motion in vivo and to identify and characterize joint laxity. In order to assess relative motion of knee joint structures, dynamic medical imaging techniques are used. Possible options are fluoroscopy, dynamic CT, and MRI. The most practical approach is fluoroscopic imaging due to the possibility of imaging knee joint structures during physical exercises at affordable costs. One of the challenges addressed in this project is the reconstruction of anatomical structures from 2D images. Via a combination of MRI and fluoroscopy data and based on the developed 3D reconstruction techniques within the project '3D From Xray' we will assess and improve skin marker-based methods for assessing skeletal dynamics and joint centers.
  
  http://www.zib.de/projects/dynamic-multi-modal-knee-joint-registration-analysis-knee-laxity

• GV-AP7

  Modeling synaptic connectivity in anatomically realistic neural networks

  Hon.-Prof. Hans-Christian Hege

  Project heads: Hon.-Prof. Hans-Christian Hege
  Project members: -
  Duration: 01.07.2014 - 31.12.2015
  Status: completed
  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-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 - 31.01.2019
  Status: completed
  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 - 31.01.2019
  Status: completed
  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-AP12

  SDModels - Structured Discrete Models as a basis for studies in geometry, numerical analysis, topology, and visualization

  Prof. Günter M. Ziegler

  Project heads: Prof. Günter M. Ziegler
  Project members: -
  Duration: 01.07.2010 - 30.06.2015
  Status: completed
  Located at: Freie Universität Berlin
  

  Description

  This project was successfully completed June 2015, after five years of intensive work, three very productive SDModels workshops, and lots of other activities. It has produced numerous successes, advanced the careers of the students and scientists involved, and produced a lot of output - a large part of this documented in scientific publications, many more papers are still under review and on the way to publication. We are happy for all the support and opportunities we had with this project, and grateful to ERC and its reviewers and administrators for making it possible. The work by SDModels is documented on these web pages. They will not be updated any more after December 2015: Please see the web pages of the scientists involved for their continuing work.
  
  The research of the SDModels project has connected traditionally quite distant fields of current mathematical research via common or structurally similar discrete (mostly: geometric) models. We thus made substantial contributions to mathematical research, by highlighting, developing, and exploiting theory for common structures and structural similarities that occur in problems/theories from diverse mathematical application areas. This involved, in particular, the areas of

  1. Discrete Geometry
  2. Discrete Differential Geometry
  3. Mesh Generation/Numerics of PDEs
  4. Topology, Topological Combinatorics
  5. Simplicial Quantum Gravity

  
  The work in the project was concentrated in three Focus Areas, namely

  • Focus Area 1: High-complexity Geometry
  • Focus Area 2: Delaunay Geometry: Polyhedral models with circle/sphere patterns
  • Focus Area 3: Topological connectivity and diameter of Discrete Structures

  
  
  http://www.mi.fu-berlin.de/math/groups/discgeom/projects/ERC/index.html

• 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: completed
  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: completed
  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-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: completed
  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: completed
  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

• GV-AP19

  Topological microstructure analysis of metal and steel grains

  PD Dr. Frank Lutz

  Project heads: PD Dr. Frank Lutz
  Project members: -
  Duration: 01.03.2014 - 31.03.2016
  Status: completed
  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/