MartinGroher

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Me

  • Dr. rer. nat. Martin Groher



  • microDimensions
    Rupert-Mayer-Str. 44
    81379 München
    Germany

Research

My research is mainly focused on the validation, registration, and segmentation of medical images, in particular:

  • enhancement, segmentation, and registration of angiographic imagery
  • vessel modeling
  • estimation and analysis of the hemodynamics of blood

  • Image-based tracking in fluoroscopic images
  • breathing motion compensation in interventional procedures

  • analysis, reconstruction, and registration of histology slices
  • validation of new imaging technologies via registration to histology

  • dynamic analysis of microscopic image sequences

Active research projects

Registration of Angiographic Images

Registration of Angiographic Images

Angiographic images visualize vascular structure in different modalities like X-Ray, CT, or MR data sets. In many medical applications, a registration and proper visualization of the data sets, especially the vasculature is useful for a better navigation. The focus of this project lies on 2D/3D registration of angiographic data where intensity-based, feature-based, and hybrid approaches are evaluated, the latter two of them requiring an accurate 2D and 3D segmentation of the data. The main clinical partner is the radiology department of the Universitätsklinikum Großhadern (Ludwig-Maximilian Universität München) , industrial partner is Siemens Medical Solutions, Forchheim.
Assessment of Fluid Tissue Interaction Using Multi-Modal Image Fusion for Characterization and Progression of Coronary Atherosclerosis

Assessment of Fluid Tissue Interaction Using Multi-Modal Image Fusion for Characterization and Progression of Coronary Atherosclerosis

Coronary artery diseases such as atherosclerosis are the leading cause of death in the industrialized world. In this project, we develop computational tools for segmentation and registration problems on intravascular images including IVUS (Intravascular Ultrasound) and OCT (Optical Coherence Tomography). One sample component of this project is Automatic Stent Implant Follow-up from Intravascular OCT Pullbacks. The stents are automatically detected and their distribution is analyzed for monitoring of the stents: their malpositioning and/or tissue growth over stent struts.
Endovascular Stenting of Aortic Aneurysms

Endovascular Stenting of Aortic Aneurysms

Endovascular stenting is a minimally invasive treatment technique for aortic aneurysms or dissections. Thereby, a certain aortic prosthesis (stent graft) is placed inside the aortic aneurysm in order to prevent a life-threatening rupture of the aortic wall. Prior to the intervention, a computed tomography angiography (CTA) is acquired on which the surgical staff can measure the parameter of the desired stent graft and finalize the intervention workflow. The entire interventional catheter navigation is done under 2D angiography imaging where the physician is missing the important 3D information. The purpose of our project is two-fold:
1. In the planning phase, a modified graph cuts algorithm automatically segments the aorta and aneurysm, so the surgical staff can choose an appropriate type of stent to match the segmented location, length, and diameter of the aneurysm and aorta. By visualizing the defined stent graft next to the three-dimensionally reconstructed aneurysm, mismeasurements can be detected in an early stage. Our main goal is the creation of an interactive simulation system that predicts the behaviour of the aortic wall and the movement of the implanted stent graft.
2. During implantation of the stent graft, after an intensity based registration of CTA and angiography data, the current navigation can be visualized in the 3D CT data set at any time. This includes solutions for electro-magnetic tracking of catheters as well as guide wires and stent grafts. Eventually, Our main goal is the creation of solutions that enable the surgeon to enhance the accuracy of the navigation and positioning, along with a minimum use of angiography, leading to less radiation exposure and less contrast agent injection.
Semi-Automatic Patellar Cartilage Segmentation

Semi-Automatic Patellar Cartilage Segmentation

Development and refinement of a software system for semi-automatic segmentation of the patellar cartilage is the main goal of this project. By providing tools for sub-pixel accurate edge tracing, automatic contour completion, and adequate visualization, a remarkable speed-up of the physicians segmentation process can be achieved. Also, improved exactness can be reached for cartilage segmentation if expertise and automation are merged in a meaningful way.
Assessment of Knee Cartilage

Assessment of Knee Cartilage

Degeneration of knee joint cartilage is an important and early indicator of osteoarthritis (OA) which is one of the major socio-economic burdens nowadays. Accurate quantification of the articular cartilage degeneration in an early stage using MR images is a promising approach in diagnosis and therapy for this disease. Particularly, volume and thickness measurement of cartilage tissue has been shown to deliver significant parameters in assessment of pathologies. Here, accurate computer-aided diagnosis tools could improve the clinical routine where image segmentation plays a crucial role. In order to overcome the time-consuming and tedious work of manual segmentation, one tries to automate the segmentation as much as possible. We focus on novel atlas-based segmentation methods for knee cartilage as well as improve today’s clinical routine of manual segmentation methods. In addition, we try to evaluate different methods for the assessment of parameters such as volume and thickness which could allow computer-aided diagnosis of knee cartilage pathologies in an early stage.

Dissertation

Abstract: Angiographic imaging is a widely used monitoring tool for minimally invasive vascular treatment and pathology access. Especially in deforming abdominal areas, the registration of pre- and intraoperative image data is still an unsolved problem, but important in several aspects. In particular, treatment time and radiation exposure to patient and physician can be significantly reduced with the resulting 2D-3D data fusion. The focus of this work is to provide methods for the registration of 2D vascular images acquired by a stationary C-arm to preoperative 3D angiographic Computed Tomography (CT) volumes, in order to improve the workflow of catheterized liver tumor treatments. Fast and robust vessel segmentation techniques are used to prepare the necessary graph data structures for a successful alignment. Here, we introduce restricted correspondence selection and iterative feature space correction to drive the proposed rigid-body algorithms to global and accurate solutions. Moreover, it is shown for the first time that the assignment of natural constraints on vessel structures allows for a successful recovery of a 3D non-rigid transformation despite a single-view scenario. Based on these results, novel volumetric visualization and roadmapping techniques are developed in order to resolve interventional problems of reduced depth perception, blind navigation, and motion blur.

M. Groher
2D-3D Registration of Angiographic Images
Technische Universtät München, 2008 (bib)

Publications

301 Moved Permanently


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Teaching

Finished and Currently active Diploma Theses, SEPs, IDPs I (co-)supervised

Available:

Current:
Finished:

Teaching Assistance

People I helped moving to a new flat while at the chair

3x Joerg Traub + one IKEA shopping...?
Tobias Sielhorst
Pierre Georgel And I live on the fifth floor without elevator, look how nice he is
Nicolas Padoy
Ben Glocker
Olivier Pauly
It's never good to have the biggest car at a chair...


UsersForm
Title: Dr.
Circumference of your head (in cm):  
Firstname: Martin
Middlename:  
Lastname: Groher
Picture: martin.jpg
Birthday:  
Nationality: Germany
Languages:  
Groups: Registration/Visualization, Medical Imaging
Expertise: Registration/Visualization, Segmentation, Medical Imaging, Computer Vision
Position: External Collaborator
Status: Alumni
Emailbefore: groher
Emailafter: cs.tum.edu
Room: microDimensions
Telephone: +49 89 289 10930
Alumniactivity: CEO of microDimensions
Defensedate: 8 April 2008
Thesistitle: 2D-3D Registration of Angiographic Images
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Edit | Attach | Refresh | Diffs | More | Revision r1.56 - 20 Mar 2013 - 15:13 - MartinGroher

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