Dr.-Ing. Stefan Zachow

Projects as a project leader

• CH16

  Reliable joint simulations for orthopaedic decision making in hip surgery

  Prof. Dr. Ralf Kornhuber / Dr.-Ing. Stefan Zachow

  Project heads: Prof. Dr. Ralf Kornhuber / Dr.-Ing. Stefan Zachow
  Project members: Dr. Jonathan Youett
  Duration: 01.06.2017 - 31.12.2018
  Status: completed
  Located at: Freie Universität Berlin
  

  Description

  This project aims at estimating the unknown parameters of a physics-based joint model together with a systematic sensitivity analysis, to ensure the reliability of a computer-assisted surgery planning tool developed in previous Matheon projects. The estimation shall be carried out using a Bayesian approach in combination with reduced basis methods to achieve feasible computing times. This calibration of the model will be accompanied by a clinical validation based on real patient data, in cooperation with the Orthopaedic Research Center of the university hospital Stavanger.
  
  http://www.mi.fu-berlin.de/en/math/groups/ag-numerik/projects/A-CH1/index.html

• CH1

  Reduced basis methods in orthopedic hip surgery planning

  Prof. Dr. Ralf Kornhuber / Dr.-Ing. Stefan Zachow

  Project heads: Prof. Dr. Ralf Kornhuber / Dr.-Ing. Stefan Zachow
  Project members: Dr. Jonathan Youett
  Duration: -
  Status: completed
  Located at: Freie Universität Berlin
  

  Description

  This project aims at the development, analysis and implementation of algorithms for computer-assisted planning in hip surgery and hip joint replacement by fast virtual test. Fast forward simulations of patient-specific motion of hip joints and implants in 3D shall be enabled by exploiting suitable a priori information. To this end, we will derive, analyze, and implement reduced basis methods for heterogeneous joint models (reduced approximation).
  
  http://www.mi.fu-berlin.de/en/math/groups/ag-numerik/projects-completed/A-CH1/index.html

• CH8

  X-ray based anatomy reconstruction with low radiation exposure

  Hon.-Prof. Hans-Christian Hege / Dr. Martin Weiser / Dr.-Ing. Stefan Zachow

  Project heads: Hon.-Prof. Hans-Christian Hege / Dr. Martin Weiser / Dr.-Ing. Stefan Zachow
  Project members: Dennis Jentsch
  Duration: -
  Status: completed
  Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin
  

  Description

  Medical imaging is essential in diagnostics and surgery planning. For representation of bony structures different imaging modalities are used; the leading methods are X-ray projection (projectional radiography) and CT. Disadvantage of these imaging techniques is the ionization caused by X-rays, particularly in CT, where the dose is 250-500 times higher than in classic X-ray projection. From the clinical perspective therefore one would like to replace CT acquisitions by a few possible X-ray projections. The project deals with the ill-posed inverse problem of 3D reconstruction of bony structures from 2D radiographs. Virtual radiographs are generated from virtual bone structure models; these are compared with clinical patient images and incrementally changed until a sufficiently accurate bone model is found whose virtual projections fit to the measured data. By using a statistical shape model as prior knowledge it is possible to formulate a well-posed optimization problem in a Bayesian setting. Using gradient methods and multilevel/multiresolution methods for both the reconstruction parameters and image data, good computational performance is achieved. Uncertainty quantification techniques can be applied to describe the spatially varying accuracy of the reconstructed model. Finding best X-ray projections (recording directions) minimizing both uncertainty and radiation exposure leads to a design of experiments problem. Two flavors of this design optimization are considered: An all-at-once approach finding the best image acquisition setup before any X-ray projections are performed, and a sequential approach determining the best next projection direction based on the accumulated knowledge gained from the previously taken images.
  
  http://www.zib.de/projects/x-ray-based-anatomy-reconstruction-low-radiation-exposure

• CH9

  Adaptive algorithms for optimization of hip implant positioning

  Dr. Martin Weiser / Dr.-Ing. Stefan Zachow

  Project heads: Dr. Martin Weiser / Dr.-Ing. Stefan Zachow
  Project members: Marian Moldenhauer
  Duration: -
  Status: completed
  Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin
  

  Description

  This project aims at a software environment supporting computer-assisted planning for total hip joint replacement by suggesting implant positions optimized for longevity of bone implants. The aim is to pre-operatively assess stress distribution in bone and to determine an optimal implant position with respect to natural function and stress distribution to prevent loosening, early migration, stress shielding, undesired bone remodeling, and fracture. Increasing the longevity of implants will help to enhance quality of life and reduce the cost of health care in aging societies. Focus of the research is the development of efficient optimization algorithms by adaptive quadrature of the high-dimensional space of daily motions and appropriate choice of tolerances for the underlying dynamic contact solver.
  
  http://www.zib.de/projects/adaptive-algorithms-optimization-hip-implant-positioning

• 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-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-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