Postdoc-Pool Projects Graz University of Technology

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Biomechanical and Microstructural Changes of the Aneurysmatic Aorta: Insights from Novel Imaging and Modeling Approaches

Contact person:
Univ.-Prof. DI. Dr. Gerhard A. Holzapfel
Institute of Biomechanics
Phone: +43 (0)316 873 – 1625

BioTechMed-Graz Postdoc: Anju Raveendran Babu, Ph.D.
Research partners:
  • Univ.-Prof. Dr.-med. Tina U. Cohnert, Department for Vascular Surgery, Medical University of Graz
  • Univ.-Prof. DI. Dr. Sepp D. Kohlwein, Institute of Molecular Biosciences, BioImaging Graz, University of Graz
This project aims to assess 3D micro-structural information on the collagen fiber organization of aneurysmatic human aortas. The method of choice is second-harmonic generation imaging; a straightforward approach was established by the consortium on non-atherosclerotic human abdominal aortic tissue samples (Schriefl et al. 2012;
Preliminary results on an abdominal aortic aneurysm sample suggest that its collagen structure is significantly altered compared to healthy arteries: the collagen fibril structure is partially lost in diseased arteries, and the dispersion along the thickness of the aortic wall becomes much more pronounced. A thorough structural analysis of healthy and diseased arteries serves the basis for biomechanical modeling and simulation.

Hardware accelerated intelligent medical imaging

Contact Person:
Univ.-Prof. Dipl.-Ing. Dr.techn. Dieter Schmalstieg
Institute for Computer Graphics and Vision
Phone.: +43 (0)316 873 – 5070
BioTechMed-Graz Postdoc: Jan Egger, Dr.rer.physiol. Dr.rer.nat.

Research partners:

  • Ao.Univ.-Prof. Rupert Portugaller, Division of Vascular and Interventional Radiology,Medical University of Graz
  • Assoz. Prof. Priv.-Doz. Philipp Stiegler, Division of Transplantation Surgery,Medical University of Graz
  • Dr. Ursula Reiter, Division of Pediatric Radiology, Medical University of Graz
  • Dr. Bernhard Kainz, Imperial College London

Segmentation of digital imagery is a labeling problem in which the goal is to assign to each pixel in an input image a unique label that represents an object. In digital image processing a broad range of applications exists, such as the detection of humans in videos or the volumetry on brain images. However, in the medical field automatic segmentation methods are typically only suitable for a specific type of pathology in a specific imaging modality and still fail too often for new acquisitions. Moreover, most automatic approaches need precise parameter settings to provide good results. As a consequence, the clinical practice in medical departments are still manual slice-by-slice segmentations which are very time consuming. Thus, interactive segmentation approaches get more and more popular, because they allow the user to support the algorithm with additional information. However, in this project an interactive graph-based segmentation approach which requires only one user-defined seed point inside the segmentation object will be developed on the GPU. The algorithm will be eligible for real-time segmentation and provide the user real-time feedback of the segmentation result.

Integrative Omics to Elucidating Lipolysis in Cancer

Contact person:
Univ.-Prof. Dr. Christoph W. Sensen
Institute of Molecular Biotechnology
Phone: +43 (0) 316 873 – 4090
BioTechMed-Graz Postdoc: Jürgen Hartler, Dipl.-Ing. Dr.techn.

Research partners:

  • Assoc.-Prof. Dr. Ruth Birner-Grünberger, Institute of Pathology, Medical University of Graz

Cancer is a leading cause of death and involves altered lipid metabolism. The rate-limiting enzyme in triacylglyceride degradation, the adipose triglyceride lipase (ATGL), might not participate only in lipid mobilization and degradation, but also in signaling pathways by generating lipid signaling molecules. Therefore, we aim at the large-scale elucidation of the functional role of ATGL in cancer progression using different omics strategies combined with computational approaches. Novel bioinformatics methods are developed for analyzing the data of molecular proteomics and metabolomics networks related to lipolysis in cancer.

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